




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     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Any:
							a    =			tf.convert_to_tensor(
							    [
							        [
							            8.2_220_991,  # 3rd highest value; idx. 0
							            -0.5_620_044,
							            5.23_229_752,
							            4.0_386_393,
							            -6.8_798_378,
							            -0.54_785_802,
							            -3.2_012_153,
							            2.92_777_176,
							            1.88_171_953,
							            7.35_341_276,  # 5th highest value; idx. 9
							            8.43_207_833,  # 2nd highest value; idx. 10
							            -9.85_711_836,
							            -5.96_209_236,
							            -1.13_039_161,
							            -7.1_115_294,
							            -0.8_369_633,
							            -5.3_186_408,
							            7.06_427_407,
							            0.81_369_344,
							            -0.82_023_817,
							            -5.9_179_796,
							            0.58_813_443,
							            -6.99_778_438,
							            4.71_551_189,
							            -0.18_771_637,
							            7.44_020_759,  # 4th highest value; idx. 25
							            9.38_450_987,  # 1st highest value; idx. 26
							            2.12_662_941,
							            -9.32_562_038,
							            2.35_652_522,
							        ],  # cummulative prob of 5 highest values <= 0.6
							        [
							            0.58_425_518,
							            4.53_139_238,
							            -5.57_510_464,
							            -6.28_030_699,
							            -7.19_529_503,
							            -4.02_122_551,
							            1.39_337_037,
							            -6.06_707_057,
							            1.59_480_517,
							            -9.643_119,
							            0.03_907_799,
							            0.67_231_762,
							            -8.88_206_726,
							            6.27_115_922,  # 4th highest value; idx. 13
							            2.28_520_723,
							            4.82_767_506,
							            4.30_421_368,
							            8.8_275_313,  # 2nd highest value; idx. 17
							            5.44_029_958,  # 5th highest value; idx. 18
							            -4.4_735_794,
							            7.38_579_536,  # 3rd highest value; idx. 20
							            -2.91_051_663,
							            2.61_946_077,
							            -2.5_674_762,
							            -9.48_959_302,
							            -4.02_922_645,
							            -1.35_416_918,
							            9.67_702_323,  # 1st highest value; idx. 27
							            -5.89_478_553,
							            1.85_370_467,
							        ],  # cummulative prob of 5 highest values <= 0.6
							    ]					,      dtype=tf.floataa					,      )

							a    =			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    =			tf.convert_to_tensor(
							    [8.222_099, 7.3_534_126, 8.432_078, 7.4_402_075, 9.38_451, 6.271_159, 8.827_531, 5.4_402_995, 7.3_857_956, 9.677_023]					,      dtype=tf.floataa					,      )  # expected non filtered values as noted above

							a    =			tf_top_k_top_p_filtering(__lowerCamelCase					,      top_k=10					,      top_p=0.6					,      min_tokens_to_keep=4							)

							a    =			output[output != -float("inf"							)]
							a    =			tf.cast(
							    tf.where(tf.not_equal(__lowerCamelCase					,      tf.constant(-float("inf"							)					,      dtype=tf.floataa							)							)							)					,      dtype=tf.intaa					,      )

							tf.debugging.assert_near(__lowerCamelCase					,      __lowerCamelCase					,      rtol=1e-12							)
							tf.debugging.assert_equal(__lowerCamelCase					,      __lowerCamelCase							)




@require_tf
class     snake_case__							(unittest.TestCase    ,       _UpperCamelCase   ):
				"""simple docstring"""



				if is_tf_available():
								SCREAMING_SNAKE_CASE_       :							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 							__UpperCAmelCase							(      self				:     Any							)					->     List[str]:
							# TF-only test: tf.saved_model export
							a    =			TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
							a    =			2
							a    =			2




							class     snake_case__							(tf.Module   ):
											"""simple docstring"""



											def __init__(      self				:     str					,      __lowerCamelCase				:     int							)					->     List[str]:
														super(__lowerCamelCase					,      self							).__init__()
														a    =			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=__lowerCamelCase					,      )
											def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Any							)					->     Optional[Any]:
														a    =			self.model.generate(
														    input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      max_new_tokens=__lowerCamelCase					,      return_dict_in_generate=__lowerCamelCase					,      )
														return {"sequences": outputs["sequences"]}

							a    =			[[2, 0], [1_02, 1_03]]
							a    =			[[1, 0], [1, 1]]
							a    =			DummyModel(model=__lowerCamelCase							)
							with tempfile.TemporaryDirectory() as tmp_dir:
										tf.saved_model.save(__lowerCamelCase					,      __lowerCamelCase					,      signatures={"serving_default": dummy_model.serving}							)
										a    =			tf.saved_model.load(__lowerCamelCase							).signatures["serving_default"]
										for batch_size in range(1					,      len(__lowerCamelCase							) + 1							):
													a    =			{
													    "input_ids": tf.constant(dummy_input_ids[:batch_size]							),
													    "attention_mask": tf.constant(dummy_attention_masks[:batch_size]							),
													}
													a    =			serving_func(**__lowerCamelCase							)["sequences"]
													a    =			test_model.generate(**__lowerCamelCase					,      max_new_tokens=__lowerCamelCase							)
													tf.debugging.assert_equal(__lowerCamelCase					,      __lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[Any]:
							# TF-only test: tf.saved_model export
							a    =			TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
							a    =			1
							a    =			2




							class     snake_case__							(tf.Module   ):
											"""simple docstring"""



											def __init__(      self				:     Union[str, Any]					,      __lowerCamelCase				:     int							)					->     Any:
														super(__lowerCamelCase					,      self							).__init__()
														a    =			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=__lowerCamelCase					,      )
											def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Tuple							)					->     str:
														a    =			self.model.generate(
														    input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      max_new_tokens=__lowerCamelCase					,      return_dict_in_generate=__lowerCamelCase					,      )
														return {"sequences": outputs["sequences"]}

							a    =			[[2], [1_02, 1_03]]
							a    =			[[1], [1, 1]]
							a    =			DummyModel(model=__lowerCamelCase							)
							with tempfile.TemporaryDirectory() as tmp_dir:
										tf.saved_model.save(__lowerCamelCase					,      __lowerCamelCase					,      signatures={"serving_default": dummy_model.serving}							)
										a    =			tf.saved_model.load(__lowerCamelCase							).signatures["serving_default"]
										for input_row in range(len(__lowerCamelCase							)							):
													a    =			{
													    "input_ids": tf.constant([dummy_input_ids[input_row]]							),
													    "attention_mask": tf.constant([dummy_attention_masks[input_row]]							),
													}
													a    =			serving_func(**__lowerCamelCase							)["sequences"]
													a    =			test_model.generate(**__lowerCamelCase					,      max_new_tokens=__lowerCamelCase							)
													tf.debugging.assert_equal(__lowerCamelCase					,      __lowerCamelCase							)





				@slow
				@require_tensorflow_text
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Union[str, Any]:
							# 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=__lowerCamelCase							)




										class     snake_case__							(tf.keras.layers.Layer   ):
														"""simple docstring"""



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





														def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Union[str, Any]					,      *__lowerCamelCase				:     Optional[Any]					,      **__lowerCamelCase				:     Dict							)					->     Optional[int]:
																	a    =			self.tokenizer.tokenize(__lowerCamelCase							)
																	a    ,		a    =			text.pad_model_inputs(
																	    __lowerCamelCase					,      max_seq_length=64					,      pad_value=self.model.config.pad_token_id							)
																	a    =			self.model.generate(input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase							)
																	return self.tokenizer.detokenize(__lowerCamelCase							)

										a    =			CompleteSentenceTransformer()
										a    =			tf.keras.layers.Input(shape=(1,)					,      dtype=tf.string					,      name="inputs"							)
										a    =			complete_model(__lowerCamelCase							)
										a    =			tf.keras.Model(__lowerCamelCase					,      __lowerCamelCase							)
										keras_model.save(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							# Has PT equivalent: this test relies on random sampling
							a    =			{
							    "do_sample": True,
							    "num_beams": 1,
							    "top_p": 0.7,
							    "top_k": 10,
							    "temperature": 0.7,
							}
							a    =			14

							a    =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
							a    =			"Hello, my dog is cute and"
							a    =			tokenizer(__lowerCamelCase					,      return_tensors="tf"							)
							a    =			TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)

							a    =			6_38
							# forces the generation to happen on CPU, to avoid GPU-related quirks
							with tf.device(":/CPU:0"							):
										tf.random.set_seed(0							)
										a    =			model.generate(**__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase							)
							self.assertTrue(expectation == len(generated_tokens[0]							)							)

							a    =			[6_38, 1_98]
							with tf.device(":/CPU:0"							):
										tf.random.set_seed(0							)
										a    =			model.generate(**__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase							)
							self.assertTrue(expectation == len(generated_tokens[0]							)							)





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




							class     snake_case__							(_UpperCamelCase   ):
											"""simple docstring"""



											def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Dict=None					,      **__lowerCamelCase				:     Any							)					->     List[str]:
														return super().call(__lowerCamelCase					,      **__lowerCamelCase							)

							a    =			FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart"							)
							a    =			bart_model.generate(__lowerCamelCase					,      foo="bar"							).numpy()
							self.assertTrue(np.array_equal(__lowerCamelCase					,      __lowerCamelCase							)							)




							class     snake_case__							(bart_model.model.encoder.__class__   ):
											"""simple docstring"""



											def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     int					,      **__lowerCamelCase				:     List[str]							)					->     Any:
														return super().call(__lowerCamelCase					,      **__lowerCamelCase							)

							a    =			FakeEncoder(bart_model.config					,      bart_model.model.shared							)
							a    =			fake_encoder

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











from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor


class       a							:



		def __init__(     self     :List[Any]   ,__lowercase     :List[Any]   ,__lowercase     :Union[str, Any]=1_3   ,__lowercase     :str=3_0   ,__lowercase     :Optional[Any]=2   ,__lowercase     :int=3   ,__lowercase     :List[Any]=True   ,__lowercase     :Tuple=True   ,__lowercase     :List[Any]=3_2   ,__lowercase     :str=2   ,__lowercase     :Union[str, Any]=4   ,__lowercase     :Dict=3_7   ,__lowercase     :List[Any]="gelu"   ,__lowercase     :Optional[int]=0.1   ,__lowercase     :str=0.1   ,__lowercase     :Union[str, Any]=1_0   ,__lowercase     :Optional[Any]=0.02   ,__lowercase     :Union[str, Any]=3   ,__lowercase     :Any=0.6   ,__lowercase     :List[str]=None   ,):
						snake_case__	:      str     		=	parent
						snake_case__	:      int     		=	batch_size
						snake_case__	:      Dict     		=	image_size
						snake_case__	:      List[str]     		=	patch_size
						snake_case__	:      str     		=	num_channels
						snake_case__	:      int     		=	is_training
						snake_case__	:      List[str]     		=	use_labels
						snake_case__	:      List[str]     		=	hidden_size
						snake_case__	:      List[Any]     		=	num_hidden_layers
						snake_case__	:      str     		=	num_attention_heads
						snake_case__	:      Any     		=	intermediate_size
						snake_case__	:      Optional[Any]     		=	hidden_act
						snake_case__	:      List[str]     		=	hidden_dropout_prob
						snake_case__	:      Union[str, Any]     		=	attention_probs_dropout_prob
						snake_case__	:      Optional[Any]     		=	type_sequence_label_size
						snake_case__	:      List[str]     		=	initializer_range
						snake_case__	:      Optional[Any]     		=	mask_ratio
						snake_case__	:      List[str]     		=	scope

						# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
						# (we add 1 for the [CLS] token)
						snake_case__	:      str     		=	(image_size // patch_size) ** 2
						snake_case__	:      List[str]     		=	int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      Optional[Any]     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

						snake_case__	:      List[Any]     		=	self.get_config()

						return config, pixel_values, labels



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						return ViTMAEConfig(
						    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   ,decoder_hidden_size=self.hidden_size   ,decoder_num_hidden_layers=self.num_hidden_layers   ,decoder_num_attention_heads=self.num_attention_heads   ,decoder_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=__lowercase   ,initializer_range=self.initializer_range   ,mask_ratio=self.mask_ratio   ,)



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :Union[str, Any]   ,__lowercase     :Dict   ,__lowercase     :List[str]  ):
						snake_case__	:      Optional[int]     		=	TFViTMAEModel(config=__lowercase  )
						snake_case__	:      int     		=	model(__lowercase   ,training=__lowercase  )
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)  )



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :List[str]   ,__lowercase     :Optional[int]   ,__lowercase     :int  ):
						snake_case__	:      Dict     		=	TFViTMAEForPreTraining(__lowercase  )
						snake_case__	:      Optional[int]     		=	model(__lowercase   ,training=__lowercase  )
						# expected sequence length = num_patches
						snake_case__	:      Optional[Any]     		=	(self.image_size // self.patch_size) ** 2
						snake_case__	:      int     		=	self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)  )

						# test greyscale images
						snake_case__	:      Tuple     		=	1
						snake_case__	:      List[Any]     		=	TFViTMAEForPreTraining(__lowercase  )

						snake_case__	:      Any     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
						snake_case__	:      Any     		=	model(__lowercase   ,training=__lowercase  )
						snake_case__	:      Any     		=	self.patch_size**2
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)  )






		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Union[str, Any]     		=	self.prepare_config_and_inputs()
						((snake_case__)		, (snake_case__)		, (snake_case__))	:      Optional[Any]     		=	config_and_inputs
						snake_case__	:      List[Any]     		=	{'''pixel_values''': pixel_values}
						return config, inputs_dict


@require_tf
class       a							(			__lowerCamelCase  ,							__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Union[str, Any]											=					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		__lowerCAmelCase	:    Union[str, Any]											=					{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

		__lowerCAmelCase	:    Tuple											=					False
		__lowerCAmelCase	:    List[Any]											=					False
		__lowerCAmelCase	:    Dict											=					False
		__lowerCAmelCase	:    List[str]											=					False



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Union[str, Any]     		=	TFViTMAEModelTester(self  )
						snake_case__	:      Dict     		=	ConfigTester(self   ,config_class=__lowercase   ,has_text_modality=__lowercase   ,hidden_size=3_7  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''  )
		def 	__lowerCamelCase			(     self     :Dict  ):
						pass



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__		, snake_case__	:      int     		=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										snake_case__	:      Union[str, Any]     		=	model_class(__lowercase  )
										self.assertIsInstance(model.get_input_embeddings()   ,(tf.keras.layers.Layer)  )
										snake_case__	:      List[Any]     		=	model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(__lowercase   ,tf.keras.layers.Layer  )  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__		, snake_case__	:      str     		=	self.model_tester.prepare_config_and_inputs_for_common()

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

										snake_case__	:      Dict     		=	['''pixel_values''']
										self.assertListEqual(arg_names[:1]   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Dict     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase  )



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						snake_case__	:      Optional[int]     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__lowercase  )



		def 	__lowerCamelCase			(     self     :int  ):
						# make the mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      Any     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      Optional[Any]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      List[str]     		=	model_class(__lowercase  )
										snake_case__	:      Union[str, Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

										snake_case__	:      Any     		=	model(__lowercase   ,noise=__lowercase  )

										snake_case__	:      Optional[int]     		=	copy.deepcopy(self._prepare_for_class(__lowercase   ,__lowercase  )  )
										snake_case__	:      List[Any]     		=	model(**__lowercase   ,noise=__lowercase  )
										snake_case__	:      Optional[Any]     		=	outputs_dict[0].numpy()
										snake_case__	:      Optional[Any]     		=	outputs_keywords[0].numpy()

										self.assertLess(np.sum(np.abs(output_dict - output_keywords  )  )   ,1e-6  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						# make the mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      List[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      List[str]     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      int     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						def prepare_numpy_arrays(__lowercase     :Dict  ):
										snake_case__	:      Any     		=	{}
										for k, v in inputs_dict.items():
														if tf.is_tensor(__lowercase  ):
																		snake_case__	:      Dict     		=	v.numpy()
														else:
																		snake_case__	:      str     		=	np.array(__lowercase  )

										return inputs_np_dict

						for model_class in self.all_model_classes:
										snake_case__	:      str     		=	model_class(__lowercase  )

										snake_case__	:      List[Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )
										snake_case__	:      Dict     		=	prepare_numpy_arrays(__lowercase  )

										snake_case__	:      Tuple     		=	model(__lowercase   ,noise=__lowercase  )
										snake_case__	:      Dict     		=	model(**__lowercase   ,noise=__lowercase  )
										self.assert_outputs_same(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Optional[Any]   ,__lowercase     :List[str]  ):
						# make masks reproducible
						np.random.seed(2  )

						snake_case__	:      Union[str, Any]     		=	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2  )
						snake_case__	:      int     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.constant(__lowercase  )

						# Add `noise` argument.
						# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
						snake_case__	:      Optional[Any]     		=	tf_noise

						super().check_pt_tf_models(__lowercase   ,__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						# make mask reproducible
						np.random.seed(2  )

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

						snake_case__	:      List[str]     		=	{
						    module_member
						    for model_class in self.all_model_classes
						    for module in (import_module(model_class.__module__  ),)
						    for module_member_name in dir(__lowercase  )
						    if module_member_name.endswith('''MainLayer'''  )
						    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
						    and module_member_name[: -len('''MainLayer'''  )] == model_class.__name__[: -len('''Model'''  )]
						    for module_member in (getattr(__lowercase   ,__lowercase  ),)
						    if isinstance(__lowercase   ,__lowercase  )
						    and tf.keras.layers.Layer in module_member.__bases__
						    and getattr(__lowercase   ,'''_keras_serializable'''   ,__lowercase  )
						}

						snake_case__	:      Optional[Any]     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      List[Any]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.convert_to_tensor(__lowercase  )
						inputs_dict.update({'''noise''': noise}  )

						for main_layer_class in tf_main_layer_classes:
										snake_case__	:      List[Any]     		=	main_layer_class(__lowercase  )

										snake_case__	:      Union[str, Any]     		=	{
										    name: tf.keras.Input(tensor.shape[1:]   ,dtype=tensor.dtype  ) for name, tensor in inputs_dict.items()
										}

										snake_case__	:      Optional[Any]     		=	tf.keras.Model(__lowercase   ,outputs=main_layer(__lowercase  )  )
										snake_case__	:      List[str]     		=	model(__lowercase  )

										with tempfile.TemporaryDirectory() as tmpdirname:
														snake_case__	:      List[str]     		=	os.path.join(__lowercase   ,'''keras_model.h5'''  )
														model.save(__lowercase  )
														snake_case__	:      List[str]     		=	tf.keras.models.load_model(
														    __lowercase   ,custom_objects={main_layer_class.__name__: main_layer_class}  )
														assert isinstance(__lowercase   ,tf.keras.Model  )
														snake_case__	:      Union[str, Any]     		=	model(__lowercase  )
														self.assert_outputs_same(__lowercase   ,__lowercase  )



		@slow
		def 	__lowerCamelCase			(     self     :Any  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      str     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      Tuple     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      Optional[int]     		=	model_class(__lowercase  )
										snake_case__	:      str     		=	self._prepare_for_class(__lowercase   ,__lowercase  )
										snake_case__	:      List[Any]     		=	model(__lowercase   ,noise=__lowercase  )

										if model_class.__name__ == "TFViTMAEModel":
														snake_case__	:      List[Any]     		=	outputs.last_hidden_state.numpy()
														snake_case__	:      List[Any]     		=	0
										else:
														snake_case__	:      Any     		=	outputs.logits.numpy()
														snake_case__	:      Tuple     		=	0

										with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(__lowercase   ,saved_model=__lowercase  )
														snake_case__	:      Optional[Any]     		=	model_class.from_pretrained(__lowercase  )
														snake_case__	:      Any     		=	model(__lowercase   ,noise=__lowercase  )

														if model_class.__name__ == "TFViTMAEModel":
																		snake_case__	:      Dict     		=	after_outputs['''last_hidden_state'''].numpy()
																		snake_case__	:      List[Any]     		=	0
														else:
																		snake_case__	:      Any     		=	after_outputs['''logits'''].numpy()
																		snake_case__	:      Optional[Any]     		=	0

														snake_case__	:      int     		=	np.amax(np.abs(out_a - out_a  )  )
														self.assertLessEqual(__lowercase   ,1e-5  )



		def 	__lowerCamelCase			(     self     :int  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      List[str]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      Dict     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      List[str]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      int     		=	model_class(__lowercase  )
										snake_case__	:      int     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

										snake_case__	:      List[Any]     		=	model(__lowercase   ,noise=__lowercase  )
										snake_case__	:      int     		=	model.get_config()
										# make sure that returned config is jsonifiable, which is required by keras
										json.dumps(__lowercase  )
										snake_case__	:      Any     		=	model_class.from_config(model.get_config()  )
										# make sure it also accepts a normal config
										snake_case__	:      Optional[int]     		=	model_class.from_config(model.config  )
										snake_case__	:      Tuple     		=	new_model(__lowercase  )  # Build model
										new_model.set_weights(model.get_weights()  )
										snake_case__	:      List[Any]     		=	new_model(__lowercase   ,noise=__lowercase  )

										self.assert_outputs_same(__lowercase   ,__lowercase  )



		@unittest.skip(
		    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''  )
		def 	__lowerCamelCase			(     self     :List[Any]  ):
						pass



		@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''  )
		def 	__lowerCamelCase			(     self     :Tuple  ):
						pass






		@slow
		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      List[Any]     		=	TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''  )
						self.assertIsNotNone(__lowercase  )



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      str     		=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
				return image





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



		@cached_property
		def 	__lowerCamelCase			(     self     :List[str]  ):
						return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''  ) if is_vision_available() else None






		@slow
		def 	__lowerCamelCase			(     self     :Any  ):
						# make random mask reproducible across the PT and TF model
						np.random.seed(2  )

						snake_case__	:      Tuple     		=	TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''  )

						snake_case__	:      int     		=	self.default_image_processor
						snake_case__	:      int     		=	prepare_img()
						snake_case__	:      Tuple     		=	image_processor(images=__lowercase   ,return_tensors='''tf'''  )

						# prepare a noise vector that will be also used for testing the TF model
						# (this way we can ensure that the PT and TF models operate on the same inputs)
						snake_case__	:      str     		=	ViTMAEConfig()
						snake_case__	:      List[str]     		=	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
						snake_case__	:      Dict     		=	np.random.uniform(size=(1, num_patches)  )

						# forward pass
						snake_case__	:      List[str]     		=	model(**__lowercase   ,noise=__lowercase  )

						# verify the logits
						snake_case__	:      Optional[Any]     		=	tf.convert_to_tensor([1, 1_9_6, 7_6_8]  )
						self.assertEqual(outputs.logits.shape   ,__lowercase  )

						snake_case__	:      Tuple     		=	tf.convert_to_tensor(
						    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

						tf.debugging.assert_near(outputs.logits[0, :3, :3]   ,__lowercase   ,atol=1e-4  )

