


'''simple docstring'''

import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
	from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class       a__				:







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase=13       ,			__lowercase=32       ,			__lowercase=2       ,			__lowercase=3       ,			__lowercase=16       ,			__lowercase=[1, 2, 1]       ,			__lowercase=[2, 2, 4]       ,			__lowercase=2       ,			__lowercase=2.0       ,			__lowercase=True       ,			__lowercase=0.0       ,			__lowercase=0.0       ,			__lowercase=0.1       ,			__lowercase="gelu"       ,			__lowercase=False       ,			__lowercase=True       ,			__lowercase=0.0_2       ,			__lowercase=1e-5       ,			__lowercase=True       ,			__lowercase=None       ,			__lowercase=True       ,			__lowercase=10       ,			__lowercase=8       ,			):
										__lowerCAmelCase							       =				parent
										__lowerCAmelCase							       =				batch_size
										__lowerCAmelCase							       =				image_size
										__lowerCAmelCase							       =				patch_size
										__lowerCAmelCase							       =				num_channels
										__lowerCAmelCase							       =				embed_dim
										__lowerCAmelCase							       =				depths
										__lowerCAmelCase							       =				num_heads
										__lowerCAmelCase							       =				window_size
										__lowerCAmelCase							       =				mlp_ratio
										__lowerCAmelCase							       =				qkv_bias
										__lowerCAmelCase							       =				hidden_dropout_prob
										__lowerCAmelCase							       =				attention_probs_dropout_prob
										__lowerCAmelCase							       =				drop_path_rate
										__lowerCAmelCase							       =				hidden_act
										__lowerCAmelCase							       =				use_absolute_embeddings
										__lowerCAmelCase							       =				patch_norm
										__lowerCAmelCase							       =				layer_norm_eps
										__lowerCAmelCase							       =				initializer_range
										__lowerCAmelCase							       =				is_training
										__lowerCAmelCase							       =				scope
										__lowerCAmelCase							       =				use_labels
										__lowerCAmelCase							       =				type_sequence_label_size
										__lowerCAmelCase							       =				encoder_stride




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

										__lowerCAmelCase							       =				self.get_config()

										return config, pixel_values, labels




				def 					_snake_case  (self		):
										return SwinvaConfig(
										    image_size=self.image_size       ,			patch_size=self.patch_size       ,			num_channels=self.num_channels       ,			embed_dim=self.embed_dim       ,			depths=self.depths       ,			num_heads=self.num_heads       ,			window_size=self.window_size       ,			mlp_ratio=self.mlp_ratio       ,			qkv_bias=self.qkv_bias       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			drop_path_rate=self.drop_path_rate       ,			hidden_act=self.hidden_act       ,			use_absolute_embeddings=self.use_absolute_embeddings       ,			path_norm=self.patch_norm       ,			layer_norm_eps=self.layer_norm_eps       ,			initializer_range=self.initializer_range       ,			encoder_stride=self.encoder_stride       ,			)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				SwinvaModel(config=__lowercase		)
										model.to(__lowercase		)
										model.eval()
										__lowerCAmelCase							       =				model(__lowercase		)

										__lowerCAmelCase							       =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
										__lowerCAmelCase							       =				int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

										self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, expected_seq_len, expected_dim)		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				SwinvaForMaskedImageModeling(config=__lowercase		)
										model.to(__lowercase		)
										model.eval()
										__lowerCAmelCase							       =				model(__lowercase		)
										self.parent.assertEqual(
										    result.logits.shape       ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

										# test greyscale images
										__lowerCAmelCase							       =				1
										__lowerCAmelCase							       =				SwinvaForMaskedImageModeling(__lowercase		)
										model.to(__lowercase		)
										model.eval()

										__lowerCAmelCase							       =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
										__lowerCAmelCase							       =				model(__lowercase		)
										self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, 1, self.image_size, self.image_size)		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				self.type_sequence_label_size
										__lowerCAmelCase							       =				SwinvaForImageClassification(__lowercase		)
										model.to(__lowercase		)
										model.eval()
										__lowerCAmelCase							       =				model(__lowercase       ,			labels=__lowercase		)
										self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.type_sequence_label_size)		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.prepare_config_and_inputs()
										__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				config_and_inputs
										__lowerCAmelCase							       =				{'''pixel_values''': pixel_values}
										return config, inputs_dict


@require_torch
class       a__				( __A							,						__A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		List[Any]	=	(
				    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
				)
				__UpperCamelCase    :		Union[str, Any]	=	(
				    {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase    :		Union[str, Any]	=	False
				__UpperCamelCase    :		Optional[Any]	=	False
				__UpperCamelCase    :		Optional[Any]	=	False
				__UpperCamelCase    :		Any	=	False




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				SwinvaModelTester(self		)
										__lowerCAmelCase							       =				ConfigTester(self       ,			config_class=__lowercase       ,			embed_dim=37		)




				def 					_snake_case  (self		):
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowercase		)




				@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''		)
				def 					_snake_case  (self		):
										pass




				@unittest.skip(reason='''Swinv2 does not use inputs_embeds'''		)
				def 					_snake_case  (self		):
										pass




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__lowerCAmelCase							       =				model_class(__lowercase		)
																self.assertIsInstance(model.get_input_embeddings()       ,			(nn.Module)		)
																__lowerCAmelCase							       =				model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(__lowercase       ,			nn.Linear		)		)




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

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

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




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase							       =				True

										for model_class in self.all_model_classes:
																__lowerCAmelCase							       =				True
																__lowerCAmelCase							       =				False
																__lowerCAmelCase							       =				True
																__lowerCAmelCase							       =				model_class(__lowercase		)
																model.to(__lowercase		)
																model.eval()
																with torch.no_grad():
																						__lowerCAmelCase							       =				model(**self._prepare_for_class(__lowercase       ,			__lowercase		)		)
																__lowerCAmelCase							       =				outputs.attentions
																__lowerCAmelCase							       =				len(self.model_tester.depths		)
																self.assertEqual(len(__lowercase		)       ,			__lowercase		)

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																__lowerCAmelCase							       =				True
																__lowerCAmelCase							       =				config.window_size**2
																__lowerCAmelCase							       =				model_class(__lowercase		)
																model.to(__lowercase		)
																model.eval()
																with torch.no_grad():
																						__lowerCAmelCase							       =				model(**self._prepare_for_class(__lowercase       ,			__lowercase		)		)
																__lowerCAmelCase							       =				outputs.attentions
																self.assertEqual(len(__lowercase		)       ,			__lowercase		)

																self.assertListEqual(
																    list(attentions[0].shape[-3:]		)       ,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,			)
																__lowerCAmelCase							       =				len(__lowercase		)

																# Check attention is always last and order is fine
																__lowerCAmelCase							       =				True
																__lowerCAmelCase							       =				True
																__lowerCAmelCase							       =				model_class(__lowercase		)
																model.to(__lowercase		)
																model.eval()
																with torch.no_grad():
																						__lowerCAmelCase							       =				model(**self._prepare_for_class(__lowercase       ,			__lowercase		)		)

																if hasattr(self.model_tester       ,			'''num_hidden_states_types'''		):
																						__lowerCAmelCase							       =				self.model_tester.num_hidden_states_types
																else:
																						# also another +1 for reshaped_hidden_states
																						__lowerCAmelCase							       =				2
																self.assertEqual(out_len + added_hidden_states       ,			len(__lowercase		)		)

																__lowerCAmelCase							       =				outputs.attentions

																self.assertEqual(len(__lowercase		)       ,			__lowercase		)

																self.assertListEqual(
																    list(self_attentions[0].shape[-3:]		)       ,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,			)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				model_class(__lowercase		)
										model.to(__lowercase		)
										model.eval()

										with torch.no_grad():
																__lowerCAmelCase							       =				model(**self._prepare_for_class(__lowercase       ,			__lowercase		)		)

										__lowerCAmelCase							       =				outputs.hidden_states

										__lowerCAmelCase							       =				getattr(
										    self.model_tester       ,			'''expected_num_hidden_layers'''       ,			len(self.model_tester.depths		) + 1		)
										self.assertEqual(len(__lowercase		)       ,			__lowercase		)

										# Swinv2 has a different seq_length
										__lowerCAmelCase							       =				(
										    config.patch_size
										    if isinstance(config.patch_size       ,			collections.abc.Iterable		)
										    else (config.patch_size, config.patch_size)
										)

										__lowerCAmelCase							       =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]		)       ,			[num_patches, self.model_tester.embed_dim]       ,			)

										__lowerCAmelCase							       =				outputs.reshaped_hidden_states
										self.assertEqual(len(__lowercase		)       ,			__lowercase		)

										__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				reshaped_hidden_states[0].shape
										__lowerCAmelCase							       =				(
										    reshaped_hidden_states[0].view(__lowercase       ,			__lowercase       ,			height * width		).permute(0       ,			2       ,			1		)
										)
										self.assertListEqual(
										    list(reshaped_hidden_states.shape[-2:]		)       ,			[num_patches, self.model_tester.embed_dim]       ,			)




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

										__lowerCAmelCase							       =				(
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size       ,			collections.abc.Iterable		)
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)

										for model_class in self.all_model_classes:
																__lowerCAmelCase							       =				True
																self.check_hidden_states_output(__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase		)

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

																self.check_hidden_states_output(__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase							       =				3

										__lowerCAmelCase							       =				(
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size       ,			collections.abc.Iterable		)
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)
										__lowerCAmelCase							       =				(
										    config.patch_size
										    if isinstance(config.patch_size       ,			collections.abc.Iterable		)
										    else (config.patch_size, config.patch_size)
										)

										__lowerCAmelCase							       =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										__lowerCAmelCase							       =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
																__lowerCAmelCase							       =				True
																self.check_hidden_states_output(__lowercase       ,			__lowercase       ,			__lowercase       ,			(padded_height, padded_width)		)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__lowerCAmelCase							       =				True
																self.check_hidden_states_output(__lowercase       ,			__lowercase       ,			__lowercase       ,			(padded_height, padded_width)		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__lowercase		)




				@slow
				def 					_snake_case  (self		):
										for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase							       =				SwinvaModel.from_pretrained(__lowercase		)
																self.assertIsNotNone(__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

										__lowerCAmelCase							       =				_config_zero_init(__lowercase		)
										for model_class in self.all_model_classes:
																__lowerCAmelCase							       =				model_class(config=__lowercase		)
																for name, param in model.named_parameters():
																						if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																												self.assertIn(
																												    ((param.data.mean() * 1e9).round() / 1e9).item()       ,			[0.0, 1.0]       ,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       ,			)


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







				"""simple docstring"""



				@cached_property
				def 					_snake_case  (self		):
										return (
										    AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''		)
										    if is_vision_available()
										    else None
										)




				@slow
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''		).to(
										    __lowercase		)
										__lowerCAmelCase							       =				self.default_image_processor

										__lowerCAmelCase							       =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										__lowerCAmelCase							       =				image_processor(images=__lowercase       ,			return_tensors='''pt'''		).to(__lowercase		)

										# forward pass
										with torch.no_grad():
																__lowerCAmelCase							       =				model(**__lowercase		)

										# verify the logits
										__lowerCAmelCase							       =				torch.Size((1, 10_00)		)
										self.assertEqual(outputs.logits.shape       ,			__lowercase		)
										__lowerCAmelCase							       =				torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]		).to(__lowercase		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,			__lowercase       ,			atol=1e-4		)		)





'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


_UpperCAmelCase				: str							    =				["""gpt2"""]
_UpperCAmelCase				: Optional[int]							    =				"""gpt2"""

if is_tf_available():


	class       a__				( tf.Module      ):







					"""simple docstring"""



					def __init__(self       ,			__lowercase		):
											super().__init__()
											__lowerCAmelCase							       =				tokenizer
											__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
											__lowerCAmelCase							       =				TFGPTaLMHeadModel.from_config(__lowercase		)




					@tf.function(input_signature=(tf.TensorSpec((None,)       ,			tf.string       ,			name='''text'''		),)		)
					def 					_snake_case  (self       ,			__lowercase		):
											__lowerCAmelCase							       =				self.tokenizer(__lowercase		)
											__lowerCAmelCase							       =				tokenized['''input_ids'''].to_tensor()

											__lowerCAmelCase							       =				tf.cast(input_ids_dense > 0       ,			tf.intaa		)
											# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

											__lowerCAmelCase							       =				self.model(input_ids=__lowercase       ,			attention_mask=__lowercase		)['''logits''']

											return outputs


@require_tf
@require_keras_nlp
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										super().setUp()

										__lowerCAmelCase							       =				[GPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
										__lowerCAmelCase							       =				[TFGPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in TOKENIZER_CHECKPOINTS]
										assert len(self.tokenizers		) == len(self.tf_tokenizers		)

										__lowerCAmelCase							       =				[
										    '''This is a straightforward English test sentence.''',
										    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
										    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
										    '''And some much more rare Chinese: 齉 堃 齉堃''',
										    '''Je vais aussi écrire en français pour tester les accents''',
										    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
										]
										__lowerCAmelCase							       =				list(zip(self.test_sentences       ,			self.test_sentences[::-1]		)		)




				def 					_snake_case  (self		):
										for tokenizer, tf_tokenizer in zip(self.tokenizers       ,			self.tf_tokenizers		):
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tokenizer([test_inputs]       ,			return_tensors='''tf'''		)
																						__lowerCAmelCase							       =				tf_tokenizer([test_inputs]		)

																						for key in python_outputs.keys():
																												# convert them to numpy to avoid messing with ragged tensors
																												__lowerCAmelCase							       =				python_outputs[key].numpy()
																												__lowerCAmelCase							       =				tf_outputs[key].numpy()

																												self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
																												self.assertTrue(tf.reduce_all(tf.cast(__lowercase       ,			tf.intaa		) == tf_outputs_values		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.function(__lowercase		)
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tf.constant(__lowercase		)
																						__lowerCAmelCase							       =				compiled_tokenizer(__lowercase		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)

																						for key in eager_outputs.keys():
																												self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				ModelToSave(tokenizer=__lowercase		)
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				model.serving(__lowercase		)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																						__lowerCAmelCase							       =				Path(__lowercase		) / '''saved.model'''
																						tf.saved_model.save(__lowercase       ,			__lowercase       ,			signatures={'''serving_default''': model.serving}		)
																						__lowerCAmelCase							       =				tf.saved_model.load(__lowercase		)
																__lowerCAmelCase							       =				loaded_model.signatures['''serving_default'''](__lowercase		)['''output_0''']
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertTrue(tf.reduce_all(out == loaded_output		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)  # Build model with some sample inputs

																__lowerCAmelCase							       =				tf_tokenizer.get_config()
																__lowerCAmelCase							       =				TFGPTaTokenizer.from_config(__lowercase		)
																__lowerCAmelCase							       =				model_from_config(__lowercase		)

																for key in from_config_output.keys():
																						self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																__lowerCAmelCase							       =				12_31_23

																for max_length in [3, 5, 10_24]:
																						__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase       ,			max_length=__lowercase		)

																						__lowerCAmelCase							       =				out['''input_ids'''].numpy().shape[1]

																						assert out_length == max_length

