


'''simple docstring'''



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 						_snake_case   (       a__		):




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.config_class(**self.inputs_dict)
								self.parent.assertTrue(hasattr(_lowerCamelCase							,							"""embed_dim"""))
								self.parent.assertTrue(hasattr(_lowerCamelCase							,							"""num_heads"""))


class 						_snake_case   :




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=64							,							_lowerCamelCase=3							,							_lowerCamelCase=[16, 48, 96]							,							_lowerCamelCase=[1, 3, 6]							,							_lowerCamelCase=[1, 2, 10]							,							_lowerCamelCase=[7, 3, 3]							,							_lowerCamelCase=[4, 2, 2]							,							_lowerCamelCase=[2, 1, 1]							,							_lowerCamelCase=[2, 2, 2]							,							_lowerCamelCase=[False, False, True]							,							_lowerCamelCase=[0.0, 0.0, 0.0]							,							_lowerCamelCase=0.02							,							_lowerCamelCase=1e-1_2							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=2							,							):
								UpperCAmelCase__					:   str       				=       parent
								UpperCAmelCase__					:   Tuple       				=       batch_size
								UpperCAmelCase__					:   Tuple       				=       image_size
								UpperCAmelCase__					:   Tuple       				=       patch_sizes
								UpperCAmelCase__					:   Optional[Any]       				=       patch_stride
								UpperCAmelCase__					:   str       				=       patch_padding
								UpperCAmelCase__					:   Union[str, Any]       				=       is_training
								UpperCAmelCase__					:   Optional[int]       				=       use_labels
								UpperCAmelCase__					:   List[str]       				=       num_labels
								UpperCAmelCase__					:   Any       				=       num_channels
								UpperCAmelCase__					:   Any       				=       embed_dim
								UpperCAmelCase__					:   List[Any]       				=       num_heads
								UpperCAmelCase__					:   Tuple       				=       stride_kv
								UpperCAmelCase__					:   Any       				=       depth
								UpperCAmelCase__					:   List[str]       				=       cls_token
								UpperCAmelCase__					:   Optional[Any]       				=       attention_drop_rate
								UpperCAmelCase__					:   List[Any]       				=       initializer_range
								UpperCAmelCase__					:   Optional[Any]       				=       layer_norm_eps




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

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

								UpperCAmelCase__					:   int       				=       self.get_config()
								return config, pixel_values, labels




					def 			snake_case__  ( self):
								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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       TFCvtModel(config=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       model(_lowerCamelCase							,							training=_lowerCamelCase)
								UpperCAmelCase__					:   List[str]       				=       (self.image_size, self.image_size)
								UpperCAmelCase__				,    UpperCAmelCase__					:   Dict       				=       image_size[0], image_size[1]
								for i in range(len(self.depth)):
											UpperCAmelCase__					:   int       				=       floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
											UpperCAmelCase__					:   Optional[Any]       				=       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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[str]       				=       self.num_labels
								UpperCAmelCase__					:   Tuple       				=       TFCvtForImageClassification(_lowerCamelCase)
								UpperCAmelCase__					:   Optional[Any]       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase							,							training=_lowerCamelCase)
								self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       self.prepare_config_and_inputs()
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   List[Any]       				=       config_and_inputs
								UpperCAmelCase__					:   Optional[Any]       				=       {"""pixel_values""": pixel_values}
								return config, inputs_dict


@require_tf
class 						_snake_case   (       a__    ,				a__    ,				unittest.TestCase		):
					lowerCAmelCase :List[str]						=   (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
					lowerCAmelCase :Dict						=   (
					    {'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase :Any						=   False
					lowerCAmelCase :Optional[int]						=   False
					lowerCAmelCase :str						=   False
					lowerCAmelCase :List[str]						=   False
					lowerCAmelCase :Any						=   False




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Tuple       				=       TFCvtModelTester(self)
								UpperCAmelCase__					:   str       				=       TFCvtConfigTester(self							,							config_class=_lowerCamelCase							,							has_text_modality=_lowerCamelCase							,							hidden_size=37)




					def 			snake_case__  ( self):
								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 			snake_case__  ( self):
								pass




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




					@unittest.skip(reason="""Cvt does not support input and output embeddings""")
					def 			snake_case__  ( self):
								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 			snake_case__  ( self):
								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 			snake_case__  ( self):
								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 			snake_case__  ( self):
								UpperCAmelCase__					:   Tuple       				=       tf.keras.mixed_precision.Policy("""mixed_float16""")
								tf.keras.mixed_precision.set_global_policy(_lowerCamelCase)
								super().test_keras_fit()
								tf.keras.mixed_precision.set_global_policy("""float32""")




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

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

											UpperCAmelCase__					:   int       				=       ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]							,							_lowerCamelCase)




					def 			snake_case__  ( self):
								def check_hidden_states_output(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
											UpperCAmelCase__					:   List[Any]       				=       model_class(_lowerCamelCase)

											UpperCAmelCase__					:   Optional[Any]       				=       model(**self._prepare_for_class(_lowerCamelCase							,							_lowerCamelCase))
											UpperCAmelCase__					:   Any       				=       outputs.hidden_states

											UpperCAmelCase__					:   Optional[int]       				=       len(self.model_tester.depth)
											self.assertEqual(len(_lowerCamelCase)							,							_lowerCamelCase)

											# 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,
											    ]							,							)

								UpperCAmelCase__				,    UpperCAmelCase__					:   Tuple       				=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase__					:   int       				=       True
											check_hidden_states_output(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											UpperCAmelCase__					:   Optional[Any]       				=       True

											check_hidden_states_output(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCamelCase)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase)




					@slow
					def 			snake_case__  ( self):
								for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCAmelCase__					:   Union[str, Any]       				=       TFCvtModel.from_pretrained(_lowerCamelCase)
											self.assertIsNotNone(_lowerCamelCase)







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   List[Any]       				=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
			return image


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




					@cached_property
					def 			snake_case__  ( self):
								return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])

								UpperCAmelCase__					:   List[str]       				=       self.default_image_processor
								UpperCAmelCase__					:   Union[str, Any]       				=       prepare_img()
								UpperCAmelCase__					:   Dict       				=       image_processor(images=_lowerCamelCase							,							return_tensors="""tf""")

								# forward pass
								UpperCAmelCase__					:   Optional[int]       				=       model(**_lowerCamelCase)

								# verify the logits
								UpperCAmelCase__					:   List[str]       				=       tf.TensorShape((1, 1000))
								self.assertEqual(outputs.logits.shape							,							_lowerCamelCase)

								UpperCAmelCase__					:   Tuple       				=       tf.constant([0.9285, 0.9015, -0.3150])
								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()							,							_lowerCamelCase							,							atol=1e-4))



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class 						_snake_case   :
					lowerCAmelCase :str						=   PegasusConfig
					lowerCAmelCase :Optional[int]						=   {}
					lowerCAmelCase :Dict						=   '''gelu'''




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=7							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase=99							,							_lowerCamelCase=32							,							_lowerCamelCase=2							,							_lowerCamelCase=4							,							_lowerCamelCase=37							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=40							,							_lowerCamelCase=2							,							_lowerCamelCase=1							,							_lowerCamelCase=0							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       parent
								UpperCAmelCase__					:   Any       				=       batch_size
								UpperCAmelCase__					:   Union[str, Any]       				=       seq_length
								UpperCAmelCase__					:   str       				=       is_training
								UpperCAmelCase__					:   Dict       				=       use_labels
								UpperCAmelCase__					:   Optional[int]       				=       vocab_size
								UpperCAmelCase__					:   int       				=       hidden_size
								UpperCAmelCase__					:   Union[str, Any]       				=       num_hidden_layers
								UpperCAmelCase__					:   str       				=       num_attention_heads
								UpperCAmelCase__					:   Any       				=       intermediate_size

								UpperCAmelCase__					:   int       				=       hidden_dropout_prob
								UpperCAmelCase__					:   Optional[Any]       				=       attention_probs_dropout_prob
								UpperCAmelCase__					:   List[str]       				=       max_position_embeddings
								UpperCAmelCase__					:   Union[str, Any]       				=       eos_token_id
								UpperCAmelCase__					:   Any       				=       pad_token_id
								UpperCAmelCase__					:   Dict       				=       bos_token_id




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size)
								UpperCAmelCase__					:   str       				=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)							,							1)
								UpperCAmelCase__					:   Union[str, Any]       				=       tf.concat([input_ids, eos_tensor]							,							axis=1)

								UpperCAmelCase__					:   int       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

								UpperCAmelCase__					:   Any       				=       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							,							**self.config_updates							,							)
								UpperCAmelCase__					:   Union[str, Any]       				=       prepare_pegasus_inputs_dict(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
								return config, inputs_dict




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[int]       				=       TFPegasusModel(config=_lowerCamelCase).get_decoder()
								UpperCAmelCase__					:   Optional[int]       				=       inputs_dict["""input_ids"""]

								UpperCAmelCase__					:   Any       				=       input_ids[:1, :]
								UpperCAmelCase__					:   List[str]       				=       inputs_dict["""attention_mask"""][:1, :]
								UpperCAmelCase__					:   int       				=       inputs_dict["""head_mask"""]
								UpperCAmelCase__					:   int       				=       1

								# first forward pass
								UpperCAmelCase__					:   int       				=       model(_lowerCamelCase							,							attention_mask=_lowerCamelCase							,							head_mask=_lowerCamelCase							,							use_cache=_lowerCamelCase)

								UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       outputs.to_tuple()

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

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

								UpperCAmelCase__					:   Union[str, Any]       				=       model(_lowerCamelCase							,							attention_mask=_lowerCamelCase)[0]
								UpperCAmelCase__					:   Any       				=       model(_lowerCamelCase							,							attention_mask=_lowerCamelCase							,							past_key_values=_lowerCamelCase)[0]

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

								# select random slice
								UpperCAmelCase__					:   Optional[Any]       				=       int(ids_tensor((1,)							,							output_from_past.shape[-1]))
								UpperCAmelCase__					:   int       				=       output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase__					:   List[str]       				=       output_from_past[:, :, random_slice_idx]

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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=None	, ):
			if attention_mask is None:
						UpperCAmelCase__					:   Any       				=       tf.cast(tf.math.not_equal(UpperCamelCase__	, config.pad_token_id   )	, tf.inta   )
			if decoder_attention_mask is None:
						UpperCAmelCase__					:   Optional[int]       				=       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:
						UpperCAmelCase__					:   Tuple       				=       tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						UpperCAmelCase__					:   Dict       				=       tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						UpperCAmelCase__					:   Tuple       				=       tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}


@require_tf
class 						_snake_case   (       a__    ,				a__    ,				unittest.TestCase		):
					lowerCAmelCase :str						=   (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
					lowerCAmelCase :Any						=   (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
					lowerCAmelCase :List[str]						=   (
					    {
					        '''conversational''': TFPegasusForConditionalGeneration,
					        '''feature-extraction''': TFPegasusModel,
					        '''summarization''': TFPegasusForConditionalGeneration,
					        '''text2text-generation''': TFPegasusForConditionalGeneration,
					        '''translation''': TFPegasusForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase :int						=   True
					lowerCAmelCase :Optional[int]						=   False
					lowerCAmelCase :Dict						=   False




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       TFPegasusModelTester(self)
								UpperCAmelCase__					:   Dict       				=       ConfigTester(self							,							config_class=_lowerCamelCase)




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




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase)


@require_sentencepiece
@require_tokenizers
@require_tf
class 						_snake_case   (       unittest.TestCase		):
					lowerCAmelCase :Dict						=   [
					    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
					    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
					]
					lowerCAmelCase :Any						=   [
					    '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
					    ''' reduce the risk of wildfires.''',
					    '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
					]  # differs slightly from pytorch, likely due to numerical differences in linear layers
					lowerCAmelCase :Tuple						=   '''google/pegasus-xsum'''




					@cached_property
					def 			snake_case__  ( self):
								return AutoTokenizer.from_pretrained(self.model_name)




					@cached_property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
								return model




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Dict       				=       self.translate_src_text(**_lowerCamelCase)
								assert self.expected_text == generated_words




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   List[Any]       				=       self.tokenizer(self.src_text							,							**_lowerCamelCase							,							padding=_lowerCamelCase							,							return_tensors="""tf""")
								UpperCAmelCase__					:   Dict       				=       self.model.generate(
								    model_inputs.input_ids							,							attention_mask=model_inputs.attention_mask							,							num_beams=2							,							use_cache=_lowerCamelCase							,							)
								UpperCAmelCase__					:   Optional[Any]       				=       self.tokenizer.batch_decode(generated_ids.numpy()							,							skip_special_tokens=_lowerCamelCase)
								return generated_words




					@slow
					def 			snake_case__  ( self):
								self._assert_generated_batch_equal_expected()