






from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFLEDForConditionalGeneration, TFLEDModel




@require_tf
class 					UpperCamelCase_ :



	'''simple docstring'''




	lowerCAmelCase						=					LEDConfig
	lowerCAmelCase						=					{}
	lowerCAmelCase						=					'''gelu'''




	def __init__(			self			,     a			,     a=13			,     a=7			,     a=True			,     a=False			,     a=99			,     a=32			,     a=2			,     a=4			,     a=37			,     a=0.1			,     a=0.1			,     a=20			,     a=2			,     a=1			,     a=0			,     a=4			,     )			->       Any:
								snake_case_           =				parent
								snake_case_           =				batch_size
								snake_case_           =				seq_length
								snake_case_           =				is_training
								snake_case_           =				use_labels
								snake_case_           =				vocab_size
								snake_case_           =				hidden_size
								snake_case_           =				num_hidden_layers
								snake_case_           =				num_attention_heads
								snake_case_           =				intermediate_size

								snake_case_           =				hidden_dropout_prob
								snake_case_           =				attention_probs_dropout_prob
								snake_case_           =				max_position_embeddings
								snake_case_           =				eos_token_id
								snake_case_           =				pad_token_id
								snake_case_           =				bos_token_id
								snake_case_           =				attention_window

								# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
								# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
								# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
								# because its local attention only attends to `self.attention_window` and one before and one after
								snake_case_           =				self.attention_window + 2

								# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
								# the `test_attention_outputs` and `test_hidden_states_output` tests
								snake_case_           =				(
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)




	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				ids_tensor([self.batch_size, self.seq_length - 1]			,     self.vocab_size   )
								snake_case_           =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )			,     1   )
								snake_case_           =				tf.concat([input_ids, eos_tensor]			,     axis=1   )

								snake_case_           =				ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size   )

								snake_case_           =				self.config_cls(
								    vocab_size=self.vocab_size			,     d_model=self.hidden_size			,     encoder_layers=self.num_hidden_layers			,     decoder_layers=self.num_hidden_layers			,     encoder_attention_heads=self.num_attention_heads			,     decoder_attention_heads=self.num_attention_heads			,     encoder_ffn_dim=self.intermediate_size			,     decoder_ffn_dim=self.intermediate_size			,     dropout=self.hidden_dropout_prob			,     attention_dropout=self.attention_probs_dropout_prob			,     max_position_embeddings=self.max_position_embeddings			,     eos_token_ids=[2]			,     bos_token_id=self.bos_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.pad_token_id			,     attention_window=self.attention_window			,     **self.config_updates			,     )
								snake_case_           =				prepare_led_inputs_dict(a			,     a			,     a   )
								snake_case_           =				tf.concat(
								    [tf.zeros_like(a   )[:, :-1], tf.ones_like(a   )[:, -1:]]			,     axis=-1			,     )
								snake_case_           =				global_attention_mask
								return config, inputs_dict






	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       Any:
								snake_case_           =				TFLEDModel(config=a   ).get_decoder()
								snake_case_           =				inputs_dict['input_ids']

								snake_case_           =				input_ids[:1, :]
								snake_case_           =				inputs_dict['attention_mask'][:1, :]
								snake_case_           =				1

								# first forward pass
								snake_case_           =				model(a			,     attention_mask=a			,     use_cache=a   )

								snake_case_     ,						snake_case_           =				outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								snake_case_           =				ids_tensor((self.batch_size, 3)			,     config.vocab_size   )
								snake_case_           =				tf.cast(ids_tensor((self.batch_size, 3)			,     2   )			,     tf.inta   )

								# append to next input_ids and
								snake_case_           =				tf.concat([input_ids, next_tokens]			,     axis=-1   )
								snake_case_           =				tf.concat([attention_mask, next_attn_mask]			,     axis=-1   )

								snake_case_           =				model(a			,     attention_mask=a   )[0]
								snake_case_           =				model(a			,     attention_mask=a			,     past_key_values=a   )[0]

								self.parent.assertEqual(next_tokens.shape[1]			,     output_from_past.shape[1]   )

								# select random slice
								snake_case_           =				int(ids_tensor((1,)			,     output_from_past.shape[-1]   )   )
								snake_case_           =				output_from_no_past[:, -3:, random_slice_idx]
								snake_case_           =				output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(a			,     a			,     rtol=1E-3   )






def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_=None				,	a_=None				,	a_=None				,	a_=None				,	):
							if attention_mask is None:
														snake_case_           =				tf.cast(tf.math.not_equal(a_				,	config.pad_token_id)				,	tf.inta)
							if decoder_attention_mask is None:
														snake_case_           =				tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape				,	dtype=tf.inta),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,	config.pad_token_id)				,	tf.inta),
														    ]				,	axis=-1				,	)
							if head_mask is None:
														snake_case_           =				tf.ones((config.encoder_layers, config.encoder_attention_heads))
							if decoder_head_mask is None:
														snake_case_           =				tf.ones((config.decoder_layers, config.decoder_attention_heads))
							return {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							    "decoder_input_ids": decoder_input_ids,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							}




@require_tf
class 					UpperCamelCase_ (     snake_case_			,   snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
	lowerCAmelCase						=					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
	lowerCAmelCase						=					(
	    {
	        '''conversational''': TFLEDForConditionalGeneration,
	        '''feature-extraction''': TFLEDModel,
	        '''summarization''': TFLEDForConditionalGeneration,
	        '''text2text-generation''': TFLEDForConditionalGeneration,
	        '''translation''': TFLEDForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase						=					True
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				TFLEDModelTester(self   )
								snake_case_           =				ConfigTester(self			,     config_class=a   )




	def 				_UpperCamelCase     (			self   )			->       Any:
								self.config_tester.run_common_tests()




	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*a   )




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_     ,						snake_case_           =				self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_           =				tf.zeros_like(inputs_dict['attention_mask']   )
								snake_case_           =				2
								snake_case_           =				tf.where(
								    tf.range(self.model_tester.seq_length   )[None, :] < num_global_attn_indices			,     1			,     inputs_dict['global_attention_mask']			,     )

								snake_case_           =				True
								snake_case_           =				self.model_tester.seq_length
								snake_case_           =				self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(a   ):
															snake_case_           =				outputs.decoder_attentions
															self.assertEqual(len(a   )			,     self.model_tester.num_hidden_layers   )
															self.assertListEqual(
															    list(decoder_attentions[0].shape[-3:]   )			,     [self.model_tester.num_attention_heads, seq_length, seq_length]			,     )

								def check_encoder_attentions_output(a   ):
															snake_case_           =				[t.numpy() for t in outputs.encoder_attentions]
															snake_case_           =				[t.numpy() for t in outputs.encoder_global_attentions]
															self.assertEqual(len(a   )			,     self.model_tester.num_hidden_layers   )
															self.assertEqual(len(a   )			,     self.model_tester.num_hidden_layers   )
															self.assertListEqual(
															    list(attentions[0].shape[-3:]   )			,     [self.model_tester.num_attention_heads, seq_length, seq_length]			,     )
															self.assertListEqual(
															    list(global_attentions[0].shape[-3:]   )			,     [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]			,     )

								for model_class in self.all_model_classes:
															snake_case_           =				True
															snake_case_           =				False
															snake_case_           =				False
															snake_case_           =				model_class(a   )
															snake_case_           =				model(self._prepare_for_class(a			,     a   )   )
															snake_case_           =				len(a   )
															self.assertEqual(config.output_hidden_states			,     a   )
															check_encoder_attentions_output(a   )

															if self.is_encoder_decoder:
																						snake_case_           =				model_class(a   )
																						snake_case_           =				model(self._prepare_for_class(a			,     a   )   )
																						self.assertEqual(config.output_hidden_states			,     a   )
																						check_decoder_attentions_output(a   )

															# Check that output attentions can also be changed via the config
															del inputs_dict["output_attentions"]
															snake_case_           =				True
															snake_case_           =				model_class(a   )
															snake_case_           =				model(self._prepare_for_class(a			,     a   )   )
															self.assertEqual(config.output_hidden_states			,     a   )
															check_encoder_attentions_output(a   )

															# Check attention is always last and order is fine
															snake_case_           =				True
															snake_case_           =				True
															snake_case_           =				model_class(a   )
															snake_case_           =				model(self._prepare_for_class(a			,     a   )   )

															self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)			,     len(a   )   )
															self.assertEqual(model.config.output_hidden_states			,     a   )
															check_encoder_attentions_output(a   )




	@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'   )
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								pass






	def 				_UpperCamelCase     (			self   )			->       Tuple:
								# TODO: Head-masking not yet implement
								pass






def 		__UpperCAmelCase     (	a_):
							return tf.constant(a_				,	dtype=tf.intaa)


lowercase    				=   1e-4




@slow
@require_tf
class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'   ).led

								# change to intended input here
								snake_case_           =				_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
								snake_case_           =				_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
								snake_case_           =				prepare_led_inputs_dict(model.config			,     a			,     a   )
								snake_case_           =				model(**a   )[0]
								snake_case_           =				(1, 10_24, 7_68)
								self.assertEqual(output.shape			,     a   )
								# change to expected output here
								snake_case_           =				tf.convert_to_tensor(
								    [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]			,     )
								tf.debugging.assert_near(output[:, :3, :3]			,     a			,     atol=1E-3   )






	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								snake_case_           =				TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'   )

								# change to intended input here
								snake_case_           =				_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
								snake_case_           =				_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
								snake_case_           =				prepare_led_inputs_dict(model.config			,     a			,     a   )
								snake_case_           =				model(**a   )[0]
								snake_case_           =				(1, 10_24, model.config.vocab_size)
								self.assertEqual(output.shape			,     a   )
								# change to expected output here
								snake_case_           =				tf.convert_to_tensor(
								    [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]			,     )
								tf.debugging.assert_near(output[:, :3, :3]			,     a			,     atol=1E-3			,     rtol=1E-3   )













from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
	from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       Any:
								snake_case_           =				self.config_class(**self.inputs_dict   )
								self.parent.assertTrue(hasattr(a			,     'embed_dim'   )   )
								self.parent.assertTrue(hasattr(a			,     'num_heads'   )   )






class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a			,     a=13			,     a=64			,     a=3			,     a=[16, 48, 96]			,     a=[1, 3, 6]			,     a=[1, 2, 10]			,     a=[7, 3, 3]			,     a=[4, 2, 2]			,     a=[2, 1, 1]			,     a=[2, 2, 2]			,     a=[False, False, True]			,     a=[0.0, 0.0, 0.0]			,     a=0.02			,     a=1E-12			,     a=True			,     a=True			,     a=2			,     )			->       List[Any]:
								snake_case_           =				parent
								snake_case_           =				batch_size
								snake_case_           =				image_size
								snake_case_           =				patch_sizes
								snake_case_           =				patch_stride
								snake_case_           =				patch_padding
								snake_case_           =				is_training
								snake_case_           =				use_labels
								snake_case_           =				num_labels
								snake_case_           =				num_channels
								snake_case_           =				embed_dim
								snake_case_           =				num_heads
								snake_case_           =				stride_kv
								snake_case_           =				depth
								snake_case_           =				cls_token
								snake_case_           =				attention_drop_rate
								snake_case_           =				initializer_range
								snake_case_           =				layer_norm_eps




	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

								snake_case_           =				None
								if self.use_labels:
															# create a random int32 tensor of given shape
															snake_case_           =				ids_tensor([self.batch_size]			,     self.num_labels   )

								snake_case_           =				self.get_config()
								return config, pixel_values, labels




	def 				_UpperCamelCase     (			self   )			->       Dict:
								return CvtConfig(
								    image_size=self.image_size			,     num_labels=self.num_labels			,     num_channels=self.num_channels			,     embed_dim=self.embed_dim			,     num_heads=self.num_heads			,     patch_sizes=self.patch_sizes			,     patch_padding=self.patch_padding			,     patch_stride=self.patch_stride			,     stride_kv=self.stride_kv			,     depth=self.depth			,     cls_token=self.cls_token			,     attention_drop_rate=self.attention_drop_rate			,     initializer_range=self.initializer_range			,     )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       int:
								snake_case_           =				TFCvtModel(config=a   )
								snake_case_           =				model(a			,     training=a   )
								snake_case_           =				(self.image_size, self.image_size)
								snake_case_     ,						snake_case_           =				image_size[0], image_size[1]
								for i in range(len(self.depth   )   ):
															snake_case_           =				floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1   )
															snake_case_           =				floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1   )
								self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.embed_dim[-1], height, width)   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       Dict:
								snake_case_           =				self.num_labels
								snake_case_           =				TFCvtForImageClassification(a   )
								snake_case_           =				model(a			,     labels=a			,     training=a   )
								self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)   )






	def 				_UpperCamelCase     (			self   )			->       Tuple:
								snake_case_           =				self.prepare_config_and_inputs()
								snake_case_     ,						snake_case_     ,						snake_case_           =				config_and_inputs
								snake_case_           =				{'pixel_values': pixel_values}
								return config, inputs_dict






@require_tf
class 					UpperCamelCase_ (     snake_case_			,   snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
	lowerCAmelCase						=					(
	    {'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				TFCvtModelTester(self   )
								snake_case_           =				TFCvtConfigTester(self			,     config_class=a			,     has_text_modality=a			,     hidden_size=37   )




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								self.config_tester.create_and_test_config_common_properties()
								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()




	@unittest.skip(reason='Cvt does not output attentions'   )
	def 				_UpperCamelCase     (			self   )			->       Dict:
								pass




	@unittest.skip(reason='Cvt does not use inputs_embeds'   )
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								pass




	@unittest.skip(reason='Cvt does not support input and output embeddings'   )
	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								pass




	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices('GPU'   )   ) == 0			,     reason='TF does not support backprop for grouped convolutions on CPU.'			,     )
	def 				_UpperCamelCase     (			self   )			->       Dict:
								super().test_dataset_conversion()




	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices('GPU'   )   ) == 0			,     reason='TF does not support backprop for grouped convolutions on CPU.'			,     )
	@slow
	def 				_UpperCamelCase     (			self   )			->       Dict:
								super().test_keras_fit()




	@unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'   )
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				tf.keras.mixed_precision.Policy('mixed_float16'   )
								tf.keras.mixed_precision.set_global_policy(a   )
								super().test_keras_fit()
								tf.keras.mixed_precision.set_global_policy('float32'   )




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_     ,						snake_case_           =				self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case_           =				['pixel_values']
															self.assertListEqual(arg_names[:1]			,     a   )




	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								def check_hidden_states_output(a			,     a			,     a   ):
															snake_case_           =				model_class(a   )

															snake_case_           =				model(**self._prepare_for_class(a			,     a   )   )
															snake_case_           =				outputs.hidden_states

															snake_case_           =				len(self.model_tester.depth   )
															self.assertEqual(len(a   )			,     a   )

															# verify the first hidden states (first block)
															self.assertListEqual(
															    list(hidden_states[0].shape[-3:]   )			,     [
															        self.model_tester.embed_dim[0],
															        self.model_tester.image_size // 4,
															        self.model_tester.image_size // 4,
															    ]			,     )

								snake_case_     ,						snake_case_           =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															snake_case_           =				True
															check_hidden_states_output(a			,     a			,     a   )

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

															check_hidden_states_output(a			,     a			,     a   )




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								snake_case_           =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a   )




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a   )






	@slow
	def 				_UpperCamelCase     (			self   )			->       int:
								for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_           =				TFCvtModel.from_pretrained(a   )
															self.assertIsNotNone(a   )






def 		__UpperCAmelCase     (	):
							snake_case_           =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
							return image




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



	'''simple docstring'''




	@cached_property
	def 				_UpperCamelCase     (			self   )			->       Any:
								return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )






	@slow
	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

								snake_case_           =				self.default_image_processor
								snake_case_           =				prepare_img()
								snake_case_           =				image_processor(images=a			,     return_tensors='tf'   )

								# forward pass
								snake_case_           =				model(**a   )

								# verify the logits
								snake_case_           =				tf.TensorShape((1, 10_00)   )
								self.assertEqual(outputs.logits.shape			,     a   )

								snake_case_           =				tf.constant([0.9_285, 0.9_015, -0.3_150]   )
								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()			,     a			,     atol=1E-4   )   )





