



'''simple docstring'''




from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    AutoTokenizer,
						    TFAutoModelForCausalLM,
						    TFAutoModelForSeqaSeqLM,
						    TFAutoModelForSpeechSeqaSeq,
						    TFAutoModelForVisionaSeq,
						    TFBartForConditionalGeneration,
						    TFLogitsProcessorList,
						    TFMinLengthLogitsProcessor,
						    tf_top_k_top_p_filtering,
						)

if is_tensorflow_text_available():
						import tensorflow_text as text







@require_tf
class 	_lowerCAmelCase							(			unittest.TestCase ):


	def     _a	(self							):
				A_	:						List[str]	=     tf.convert_to_tensor(
				    [
				        [
				            8.2_22_09_91,  # 3rd highest value; idx. 0
				            -0.5_62_00_44,
				            5.23_22_97_52,
				            4.0_38_63_93,
				            -6.8_79_83_78,
				            -0.54_78_58_02,
				            -3.2_01_21_53,
				            2.92_77_71_76,
				            1.88_17_19_53,
				            7.35_34_12_76,  # 5th highest value; idx. 9
				            8.43_20_78_33,  # 2nd highest value; idx. 10
				            -9.85_71_18_36,
				            -5.96_20_92_36,
				            -1.13_03_91_61,
				            -7.1_11_52_94,
				            -0.8_36_96_33,
				            -5.3_18_64_08,
				            7.06_42_74_07,
				            0.81_36_93_44,
				            -0.82_02_38_17,
				            -5.9_17_97_96,
				            0.58_81_34_43,
				            -6.99_77_84_38,
				            4.71_55_11_89,
				            -0.18_77_16_37,
				            7.44_02_07_59,  # 4th highest value; idx. 25
				            9.38_45_09_87,  # 1st highest value; idx. 26
				            2.12_66_29_41,
				            -9.32_56_20_38,
				            2.35_65_25_22,
				        ],  # cummulative prob of 5 highest values <= 0.6
				        [
				            0.58_42_55_18,
				            4.53_13_92_38,
				            -5.57_51_04_64,
				            -6.28_03_06_99,
				            -7.19_52_95_03,
				            -4.02_12_25_51,
				            1.39_33_70_37,
				            -6.06_70_70_57,
				            1.59_48_05_17,
				            -9.64_31_19,
				            0.03_90_77_99,
				            0.67_23_17_62,
				            -8.88_20_67_26,
				            6.27_11_59_22,  # 4th highest value; idx. 13
				            2.28_52_07_23,
				            4.82_76_75_06,
				            4.30_42_13_68,
				            8.8_27_53_13,  # 2nd highest value; idx. 17
				            5.44_02_99_58,  # 5th highest value; idx. 18
				            -4.4_73_57_94,
				            7.38_57_95_36,  # 3rd highest value; idx. 20
				            -2.91_05_16_63,
				            2.61_94_60_77,
				            -2.5_67_47_62,
				            -9.48_95_93_02,
				            -4.02_92_26_45,
				            -1.35_41_69_18,
				            9.67_70_23_23,  # 1st highest value; idx. 27
				            -5.89_47_85_53,
				            1.85_37_04_67,
				        ],  # cummulative prob of 5 highest values <= 0.6
				    ]       ,      dtype=tf.floataa       ,      )

				A_	:						str	=     tf.convert_to_tensor(
				    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]       ,      dtype=tf.intaa       ,      )  # expected non filtered idx as noted above

				A_	:						Optional[int]	=     tf.convert_to_tensor(
				    [8.22_20_99, 7.3_53_41_26, 8.43_20_78, 7.4_40_20_75, 9.3_84_51, 6.27_11_59, 8.82_75_31, 5.4_40_29_95, 7.3_85_79_56, 9.67_70_23]       ,      dtype=tf.floataa       ,      )  # expected non filtered values as noted above

				A_	:						int	=     tf_top_k_top_p_filtering(lowercase       ,      top_k=10       ,      top_p=0.6       ,      min_tokens_to_keep=4							)

				A_	:						int	=     output[output != -float("""inf"""							)]
				A_	:						Any	=     tf.cast(
				    tf.where(tf.not_equal(lowercase       ,      tf.constant(-float("""inf"""							)       ,      dtype=tf.floataa							)							)							)       ,      dtype=tf.intaa       ,      )

				tf.debugging.assert_near(lowercase       ,      lowercase       ,      rtol=1E-12							)
				tf.debugging.assert_equal(lowercase       ,      lowercase							)







@require_tf
class 	_lowerCAmelCase							(			unittest.TestCase      ,			__UpperCAmelCase ):
	# setting framework_dependent_parameters needs to be gated, just like its contents' imports
	if is_tf_available():
		__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					{
		    'AutoModelForCausalLM': TFAutoModelForCausalLM,
		    'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
		    'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
		    'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
		    'LogitsProcessorList': TFLogitsProcessorList,
		    'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
		    'create_tensor_fn': tf.convert_to_tensor,
		    'floats_tensor': floats_tensor,
		    'return_tensors': 'tf',
		}


	@slow
	def     _a	(self							):
				# TF-only test: tf.saved_model export
				A_	:						Any	=     TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
				A_	:						List[Any]	=     2
				A_	:						Optional[Any]	=     2







				class 	_lowerCAmelCase							(			tf.Module ):


					def __init__(self       ,      lowercase							):
								super(lowercase       ,      self							).__init__()
								A_	:						List[str]	=     model


					@tf.function(
					    input_signature=(
					        tf.TensorSpec((None, input_length)       ,      tf.intaa       ,      name="""input_ids"""							),
					        tf.TensorSpec((None, input_length)       ,      tf.intaa       ,      name="""attention_mask"""							),
					    )       ,      jit_compile=lowercase       ,      )
					def     _a	(self       ,      lowercase       ,      lowercase							):
								A_	:						Union[str, Any]	=     self.model.generate(
								    input_ids=lowercase       ,      attention_mask=lowercase       ,      max_new_tokens=lowercase       ,      return_dict_in_generate=lowercase       ,      )
								return {"sequences": outputs["sequences"]}






				A_	:						str	=     [[2, 0], [102, 103]]
				A_	:						Tuple	=     [[1, 0], [1, 1]]
				A_	:						List[str]	=     DummyModel(model=lowercase							)
				with tempfile.TemporaryDirectory() as tmp_dir:
							tf.saved_model.save(lowercase       ,      lowercase       ,      signatures={"""serving_default""": dummy_model.serving}							)
							A_	:						str	=     tf.saved_model.load(lowercase							).signatures["""serving_default"""]
							for batch_size in range(1       ,      len(lowercase							) + 1							):
										A_	:						List[Any]	=     {
										    """input_ids""": tf.constant(dummy_input_ids[:batch_size]							),
										    """attention_mask""": tf.constant(dummy_attention_masks[:batch_size]							),
										}
										A_	:						Tuple	=     serving_func(**lowercase							)["""sequences"""]
										A_	:						Any	=     test_model.generate(**lowercase       ,      max_new_tokens=lowercase							)
										tf.debugging.assert_equal(lowercase       ,      lowercase							)


	@slow
	def     _a	(self							):
				# TF-only test: tf.saved_model export
				A_	:						List[str]	=     TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
				A_	:						Union[str, Any]	=     1
				A_	:						Optional[int]	=     2







				class 	_lowerCAmelCase							(			tf.Module ):


					def __init__(self       ,      lowercase							):
								super(lowercase       ,      self							).__init__()
								A_	:						Tuple	=     model


					@tf.function(
					    input_signature=(
					        tf.TensorSpec((batch_size, None)       ,      tf.intaa       ,      name="""input_ids"""							),
					        tf.TensorSpec((batch_size, None)       ,      tf.intaa       ,      name="""attention_mask"""							),
					    )       ,      jit_compile=lowercase       ,      )
					def     _a	(self       ,      lowercase       ,      lowercase							):
								A_	:						Dict	=     self.model.generate(
								    input_ids=lowercase       ,      attention_mask=lowercase       ,      max_new_tokens=lowercase       ,      return_dict_in_generate=lowercase       ,      )
								return {"sequences": outputs["sequences"]}






				A_	:						int	=     [[2], [102, 103]]
				A_	:						Dict	=     [[1], [1, 1]]
				A_	:						Tuple	=     DummyModel(model=lowercase							)
				with tempfile.TemporaryDirectory() as tmp_dir:
							tf.saved_model.save(lowercase       ,      lowercase       ,      signatures={"""serving_default""": dummy_model.serving}							)
							A_	:						Union[str, Any]	=     tf.saved_model.load(lowercase							).signatures["""serving_default"""]
							for input_row in range(len(lowercase							)							):
										A_	:						Union[str, Any]	=     {
										    """input_ids""": tf.constant([dummy_input_ids[input_row]]							),
										    """attention_mask""": tf.constant([dummy_attention_masks[input_row]]							),
										}
										A_	:						Optional[Any]	=     serving_func(**lowercase							)["""sequences"""]
										A_	:						int	=     test_model.generate(**lowercase       ,      max_new_tokens=lowercase							)
										tf.debugging.assert_equal(lowercase       ,      lowercase							)


	@slow
	@require_tensorflow_text
	def     _a	(self							):
				# TF-only test: tf.saved_model export
				with tempfile.TemporaryDirectory() as tmp_dir:
							# file needed to load the TF tokenizer
							hf_hub_download(repo_id="""google/flan-t5-small"""       ,      filename="""spiece.model"""       ,      local_dir=lowercase							)







							class 	_lowerCAmelCase							(			tf.keras.layers.Layer ):


								def __init__(self							):
											super().__init__()
											A_	:						Any	=     text.SentencepieceTokenizer(
											    model=tf.io.gfile.GFile(os.path.join(lowercase       ,      """spiece.model"""							)       ,      """rb"""							).read()							)
											A_	:						List[Any]	=     TFAutoModelForSeqaSeqLM.from_pretrained("""hf-internal-testing/tiny-random-t5"""							)


								def     _a	(self       ,      lowercase       ,      *lowercase       ,      **lowercase							):
											A_	:						Optional[Any]	=     self.tokenizer.tokenize(lowercase							)
											A_,    A_	:						int	=     text.pad_model_inputs(
											    lowercase       ,      max_seq_length=64       ,      pad_value=self.model.config.pad_token_id							)
											A_	:						Dict	=     self.model.generate(input_ids=lowercase       ,      attention_mask=lowercase							)
											return self.tokenizer.detokenize(lowercase							)






							A_	:						Optional[Any]	=     CompleteSentenceTransformer()
							A_	:						Union[str, Any]	=     tf.keras.layers.Input(shape=(1,)       ,      dtype=tf.string       ,      name="""inputs"""							)
							A_	:						Optional[int]	=     complete_model(lowercase							)
							A_	:						Union[str, Any]	=     tf.keras.Model(lowercase       ,      lowercase							)
							keras_model.save(lowercase							)


	def     _a	(self							):
				# Has PT equivalent: this test relies on random sampling
				A_	:						int	=     {
				    """do_sample""": True,
				    """num_beams""": 1,
				    """top_p""": 0.7,
				    """top_k""": 10,
				    """temperature""": 0.7,
				}
				A_	:						List[str]	=     14

				A_	:						List[Any]	=     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
				A_	:						Tuple	=     """Hello, my dog is cute and"""
				A_	:						str	=     tokenizer(lowercase       ,      return_tensors="""tf"""							)
				A_	:						Any	=     TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)

				A_	:						Tuple	=     638
				# forces the generation to happen on CPU, to avoid GPU-related quirks
				with tf.device(""":/CPU:0"""							):
							tf.random.set_seed(0							)
							A_	:						Dict	=     model.generate(**lowercase       ,      eos_token_id=lowercase       ,      **lowercase							)
				self.assertTrue(expectation == len(generated_tokens[0]							)							)

				A_	:						Union[str, Any]	=     [638, 198]
				with tf.device(""":/CPU:0"""							):
							tf.random.set_seed(0							)
							A_	:						str	=     model.generate(**lowercase       ,      eos_token_id=lowercase       ,      **lowercase							)
				self.assertTrue(expectation == len(generated_tokens[0]							)							)


	def     _a	(self							):
				# Has PT equivalent: ample use of framework-specific code
				A_	:						List[Any]	=     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bart"""							)
				A_	:						Tuple	=     """Hugging Face is a technology company based in New York and Paris."""
				A_	:						List[Any]	=     bart_tokenizer(lowercase       ,      return_tensors="""tf"""							).input_ids
				A_	:						Tuple	=     TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart"""							)
				A_	:						Union[str, Any]	=     bart_model.generate(lowercase							).numpy()







				class 	_lowerCAmelCase							(			__UpperCAmelCase ):


					def     _a	(self       ,      lowercase       ,      lowercase=None       ,      **lowercase							):
								return super().call(lowercase       ,      **lowercase							)






				A_	:						int	=     FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart"""							)
				A_	:						Any	=     bart_model.generate(lowercase       ,      foo="""bar"""							).numpy()
				self.assertTrue(np.array_equal(lowercase       ,      lowercase							)							)







				class 	_lowerCAmelCase							(			bart_model.model.encoder.__class__ ):


					def     _a	(self       ,      lowercase       ,      **lowercase							):
								return super().call(lowercase       ,      **lowercase							)






				A_	:						str	=     FakeEncoder(bart_model.config       ,      bart_model.model.shared							)
				A_	:						Dict	=     fake_encoder

				# Normal generation still works (the output will be different because the encoder weights are different)
				A_	:						Optional[Any]	=     bart_model.generate(lowercase							).numpy()
				with self.assertRaises(lowercase							):
							# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
							bart_model.generate(lowercase       ,      foo="""bar"""							)




'''simple docstring'''




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwiftFormerForImageClassification, SwiftFormerModel
						from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=3       ,      lowercase=True       ,      lowercase=True       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=224       ,      lowercase=1000       ,      lowercase=[3, 3, 6, 4]       ,      lowercase=[48, 56, 112, 220]       ,      ):
				A_	:						Dict	=     parent
				A_	:						List[Any]	=     batch_size
				A_	:						Dict	=     num_channels
				A_	:						Optional[Any]	=     is_training
				A_	:						List[str]	=     use_labels
				A_	:						List[Any]	=     hidden_dropout_prob
				A_	:						Optional[int]	=     attention_probs_dropout_prob
				A_	:						Tuple	=     num_labels
				A_	:						List[str]	=     image_size
				A_	:						str	=     layer_depths
				A_	:						Optional[int]	=     embed_dims


	def     _a	(self							):
				A_	:						Union[str, Any]	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				A_	:						int	=     None
				if self.use_labels:
							A_	:						Tuple	=     ids_tensor([self.batch_size]       ,      self.num_labels							)

				A_	:						int	=     self.get_config()

				return config, pixel_values, labels


	def     _a	(self							):
				return SwiftFormerConfig(
				    depths=self.layer_depths       ,      embed_dims=self.embed_dims       ,      mlp_ratio=4       ,      downsamples=[True, True, True, True]       ,      hidden_act="""gelu"""       ,      num_labels=self.num_labels       ,      down_patch_size=3       ,      down_stride=2       ,      down_pad=1       ,      drop_rate=0.0       ,      drop_path_rate=0.0       ,      use_layer_scale=lowercase       ,      layer_scale_init_value=1E-5       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     SwiftFormerModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Union[str, Any]	=     model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.embed_dims[-1], 7, 7)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						Any	=     self.num_labels
				A_	:						Any	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Optional[int]	=     model(lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)

				A_	:						int	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_	:						int	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
				A_	:						Dict	=     model(lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)


	def     _a	(self							):
				((A_),    (A_),    (A_))	:						int	=     self.prepare_config_and_inputs()
				A_	:						Any	=     {"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__SCREAMING_SNAKE_CASE   :		int					=					False
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					False
	__SCREAMING_SNAKE_CASE   :		List[str]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False


	def     _a	(self							):
				A_	:						Optional[int]	=     SwiftFormerModelTester(self							)
				A_	:						Any	=     ConfigTester(
				    self       ,      config_class=lowercase       ,      has_text_modality=lowercase       ,      hidden_size=37       ,      num_attention_heads=12       ,      num_hidden_layers=12       ,      )


	def     _a	(self							):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						Optional[Any]	=     model_class(lowercase							)
							A_	:						Optional[int]	=     model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase       ,      nn.Linear							)							)


	def     _a	(self							):
				A_,    A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						str	=     model_class(lowercase							)
							A_	:						Optional[int]	=     inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A_	:						int	=     [*signature.parameters.keys()]

							A_	:						Tuple	=     ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]       ,      lowercase							)


	def     _a	(self							):
				A_	:						str	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				A_	:						List[Any]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	@slow
	def     _a	(self							):
				for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_	:						Optional[Any]	=     SwiftFormerModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)


	@unittest.skip(reason="""SwiftFormer does not output attentions"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				def check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							):
							A_	:						str	=     model_class(lowercase							)
							model.to(lowercase							)
							model.eval()

							with torch.no_grad():
										A_	:						Optional[int]	=     model(**self._prepare_for_class(lowercase       ,      lowercase							)							)

							A_	:						Any	=     outputs.hidden_states

							A_	:						Any	=     8
							self.assertEqual(len(lowercase							)       ,      lowercase							)  # TODO

							# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
							# with the width and height being successively divided by 2, after every 2 blocks
							for i in range(len(lowercase							)							):
										self.assertEqual(
										    hidden_states[i].shape       ,      torch.Size(
										        [
										            self.model_tester.batch_size,
										            self.model_tester.embed_dims[i // 2],
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										        ]							)       ,      )

				A_,    A_	:						str	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						str	=     True
							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)

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

							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)


	def     _a	(self							):
				def _config_zero_init(lowercase							):
							A_	:						Optional[Any]	=     copy.deepcopy(lowercase							)
							for key in configs_no_init.__dict__.keys():
										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
													setattr(lowercase       ,      lowercase       ,      1E-10							)
										if isinstance(getattr(lowercase       ,      lowercase       ,      lowercase							)       ,      lowercase							):
													A_	:						Any	=     _config_zero_init(getattr(lowercase       ,      lowercase							)							)
													setattr(lowercase       ,      lowercase       ,      lowercase							)
							return configs_no_init

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

				A_	:						Any	=     _config_zero_init(lowercase							)
				for model_class in self.all_model_classes:
							A_	:						List[str]	=     model_class(config=lowercase							)
							for name, param in model.named_parameters():
										if param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1E9) / 1E9).round().item()       ,      [0.0, 1.0]       ,      msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,      )


	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
	def     _a	(self							):
				pass






def 	a			( ):
			'''simple docstring'''





			A_	:						List[str]	=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image







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


	@cached_property
	def     _a	(self							):
				return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None


	@slow
	def     _a	(self							):
				A_	:						Any	=     SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(lowercase							)

				A_	:						Dict	=     self.default_image_processor
				A_	:						Dict	=     prepare_img()
				A_	:						int	=     image_processor(images=lowercase       ,      return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_	:						int	=     model(**lowercase							)

				# verify the logits
				A_	:						int	=     torch.Size((1, 1000)							)
				self.assertEqual(outputs.logits.shape       ,      lowercase							)

				A_	:						List[str]	=     torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]							).to(lowercase							)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,      lowercase       ,      atol=1E-4							)							)