






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  :
			_lowercase		:    List[str]			     =							PegasusConfig
			_lowercase		:    List[str]			     =							{}
			_lowercase		:    List[str]			     =							'''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=40		,					a=2		,					a=1		,					a=0		,					)      ->       Any:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= seq_length
						SCREAMING_SNAKE_CASE						= is_training
						SCREAMING_SNAKE_CASE						= use_labels
						SCREAMING_SNAKE_CASE						= vocab_size
						SCREAMING_SNAKE_CASE						= hidden_size
						SCREAMING_SNAKE_CASE						= num_hidden_layers
						SCREAMING_SNAKE_CASE						= num_attention_heads
						SCREAMING_SNAKE_CASE						= intermediate_size

						SCREAMING_SNAKE_CASE						= hidden_dropout_prob
						SCREAMING_SNAKE_CASE						= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE						= max_position_embeddings
						SCREAMING_SNAKE_CASE						= eos_token_id
						SCREAMING_SNAKE_CASE						= pad_token_id
						SCREAMING_SNAKE_CASE						= bos_token_id






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

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

						SCREAMING_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		,					**self.config_updates		,					)
						SCREAMING_SNAKE_CASE						= prepare_pegasus_inputs_dict(a		,					a		,					a)
						return config, inputs_dict







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       str:
						SCREAMING_SNAKE_CASE						= TFPegasusModel(config=a).get_decoder()
						SCREAMING_SNAKE_CASE						= inputs_dict['input_ids']

						SCREAMING_SNAKE_CASE						= input_ids[:1, :]
						SCREAMING_SNAKE_CASE						= inputs_dict['attention_mask'][:1, :]
						SCREAMING_SNAKE_CASE						= inputs_dict['head_mask']
						SCREAMING_SNAKE_CASE						= 1

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

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= outputs.to_tuple()

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

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

						SCREAMING_SNAKE_CASE						= model(a		,					attention_mask=a)[0]
						SCREAMING_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
						SCREAMING_SNAKE_CASE						= int(ids_tensor((1,)		,					output_from_past.shape[-1]))
						SCREAMING_SNAKE_CASE						= output_from_no_past[:, -3:, random_slice_idx]
						SCREAMING_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 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None							,       ):
			if attention_mask is None:
						SCREAMING_SNAKE_CASE						= tf.cast(tf.math.not_equal(_UpperCAmelCase							,       config.pad_token_id)							,       tf.inta)
			if decoder_attention_mask is None:
						SCREAMING_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:
						SCREAMING_SNAKE_CASE						= tf.ones((config.encoder_layers, config.encoder_attention_heads))
			if decoder_head_mask is None:
						SCREAMING_SNAKE_CASE						= tf.ones((config.decoder_layers, config.decoder_attention_heads))
			if cross_attn_head_mask is None:
						SCREAMING_SNAKE_CASE						= 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					):
			_lowercase		:    List[Any]			     =							(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
			_lowercase		:    Dict			     =							(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
			_lowercase		:    List[Any]			     =							(
			    {
			        '''conversational''': TFPegasusForConditionalGeneration,
			        '''feature-extraction''': TFPegasusModel,
			        '''summarization''': TFPegasusForConditionalGeneration,
			        '''text2text-generation''': TFPegasusForConditionalGeneration,
			        '''translation''': TFPegasusForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			_lowercase		:    Any			     =							True
			_lowercase		:    List[str]			     =							False
			_lowercase		:    Optional[int]			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= TFPegasusModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						self.config_tester.run_common_tests()







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*a)



@require_sentencepiece
@require_tokenizers
@require_tf
class 		_snake_case  (  unittest.TestCase					):
			_lowercase		:    Optional[int]			     =							[
			    ''' 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!" ''',
			]
			_lowercase		:    Optional[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
			_lowercase		:    List[Any]			     =							'''google/pegasus-xsum'''






			@cached_property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						return AutoTokenizer.from_pretrained(self.model_name)






			@cached_property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
						return model






			def    SCREAMING_SNAKE_CASE__    (       self		,					**a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.translate_src_text(**a)
						assert self.expected_text == generated_words






			def    SCREAMING_SNAKE_CASE__    (       self		,					**a)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.tokenizer(self.src_text		,					**a		,					padding=a		,					return_tensors='tf')
						SCREAMING_SNAKE_CASE						= self.model.generate(
						    model_inputs.input_ids		,					attention_mask=model_inputs.attention_mask		,					num_beams=2		,					use_cache=a		,					)
						SCREAMING_SNAKE_CASE						= self.tokenizer.batch_decode(generated_ids.numpy()		,					skip_special_tokens=a)
						return generated_words







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						self._assert_generated_batch_equal_expected()












import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 		_snake_case  :






			def __init__(       self		,					a		,					a=99		,					a=13		,					a=7		,					a=9		,					a=True		,					a=True		,					a=False		,					a=32		,					a=5		,					a=4		,					a=37		,					a=8		,					a=0.1		,					a=0.0_02		,					a=1		,					a=0		,					a=0		,					a=None		,					a=None		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= encoder_seq_length
						SCREAMING_SNAKE_CASE						= decoder_seq_length
						# For common tests
						SCREAMING_SNAKE_CASE						= self.decoder_seq_length
						SCREAMING_SNAKE_CASE						= is_training
						SCREAMING_SNAKE_CASE						= use_attention_mask
						SCREAMING_SNAKE_CASE						= use_labels
						SCREAMING_SNAKE_CASE						= vocab_size
						SCREAMING_SNAKE_CASE						= hidden_size
						SCREAMING_SNAKE_CASE						= num_hidden_layers
						SCREAMING_SNAKE_CASE						= num_attention_heads
						SCREAMING_SNAKE_CASE						= d_ff
						SCREAMING_SNAKE_CASE						= relative_attention_num_buckets
						SCREAMING_SNAKE_CASE						= dropout_rate
						SCREAMING_SNAKE_CASE						= initializer_factor
						SCREAMING_SNAKE_CASE						= eos_token_id
						SCREAMING_SNAKE_CASE						= pad_token_id
						SCREAMING_SNAKE_CASE						= decoder_start_token_id
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= decoder_layers






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						return TaConfig.from_pretrained('google/umt5-base')






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a=None		,					a=None		,					a=None		,					a=None		,					a=None		,					)      ->       Optional[int]:
						if attention_mask is None:
									SCREAMING_SNAKE_CASE						= input_ids.ne(config.pad_token_id)
						if decoder_attention_mask is None:
									SCREAMING_SNAKE_CASE						= decoder_input_ids.ne(config.pad_token_id)
						if head_mask is None:
									SCREAMING_SNAKE_CASE						= torch.ones(config.num_hidden_layers		,					config.num_attention_heads		,					device=a)
						if decoder_head_mask is None:
									SCREAMING_SNAKE_CASE						= torch.ones(config.num_decoder_layers		,					config.num_attention_heads		,					device=a)
						if cross_attn_head_mask is None:
									SCREAMING_SNAKE_CASE						= torch.ones(
									    config.num_decoder_layers		,					config.num_attention_heads		,					device=a)
						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,
						}






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.encoder_seq_length]		,					self.vocab_size)
						SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.decoder_seq_length]		,					self.vocab_size)

						# we need to clamp the input ids here to avoid having pad token in between
						# this is because for NllbMoe the position_ids are prepared such that
						# all pad tokens have pos id = 2 and rest are between 2..seq_length
						# and the seq_length here is seq_length - num_pad_tokens
						# but when using past, there is no way of knowing if the past input ids had
						# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
						# position_ids being off by num_pad_tokens in past input
						SCREAMING_SNAKE_CASE						= input_ids.clamp(self.pad_token_id + 1)
						SCREAMING_SNAKE_CASE						= decoder_input_ids.clamp(self.pad_token_id + 1)

						SCREAMING_SNAKE_CASE						= self.get_config()
						SCREAMING_SNAKE_CASE						= config.num_attention_heads
						SCREAMING_SNAKE_CASE						= self.prepare_inputs_dict(a		,					a		,					a)
						return config, input_dict






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
						return config, inputs_dict






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						return TaConfig(
						    vocab_size=166		,					d_model=self.hidden_size		,					d_ff=self.d_ff		,					d_kv=self.hidden_size // self.num_attention_heads		,					num_layers=self.num_hidden_layers		,					num_decoder_layers=self.decoder_layers		,					num_heads=self.num_attention_heads		,					relative_attention_num_buckets=self.relative_attention_num_buckets		,					dropout_rate=self.dropout_rate		,					initializer_factor=self.initializer_factor		,					eos_token_id=self.eos_token_id		,					bos_token_id=self.pad_token_id		,					pad_token_id=self.pad_token_id		,					decoder_start_token_id=self.decoder_start_token_id		,					)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return TaConfig(
						    vocab_size=self.vocab_size		,					d_model=self.hidden_size		,					d_ff=self.d_ff		,					d_kv=self.hidden_size // self.num_attention_heads		,					num_layers=self.num_hidden_layers		,					num_decoder_layers=self.decoder_layers		,					num_heads=self.num_attention_heads		,					relative_attention_num_buckets=self.relative_attention_num_buckets		,					dropout_rate=self.dropout_rate		,					initializer_factor=self.initializer_factor		,					eos_token_id=self.eos_token_id		,					bos_token_id=self.pad_token_id		,					pad_token_id=self.pad_token_id		,					decoder_start_token_id=self.decoder_start_token_id		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= UMTaModel(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= model(
						    input_ids=a		,					decoder_input_ids=a		,					attention_mask=a		,					decoder_attention_mask=a		,					)
						SCREAMING_SNAKE_CASE						= model(input_ids=a		,					decoder_input_ids=a)
						SCREAMING_SNAKE_CASE						= result.last_hidden_state
						SCREAMING_SNAKE_CASE						= result.past_key_values
						SCREAMING_SNAKE_CASE						= result.encoder_last_hidden_state

						self.parent.assertEqual(encoder_output.size()		,					(self.batch_size, self.encoder_seq_length, self.hidden_size))
						self.parent.assertEqual(decoder_output.size()		,					(self.batch_size, self.decoder_seq_length, self.hidden_size))
						# There should be `num_layers` key value embeddings stored in decoder_past
						self.parent.assertEqual(len(a)		,					config.num_layers)
						# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
						self.parent.assertEqual(len(decoder_past[0])		,					4)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= UMTaModel(config=a).get_decoder().to(a).eval()
						# first forward pass
						SCREAMING_SNAKE_CASE						= model(a		,					use_cache=a)
						SCREAMING_SNAKE_CASE						= model(a)
						SCREAMING_SNAKE_CASE						= model(a		,					use_cache=a)

						self.parent.assertTrue(len(a) == len(a))
						self.parent.assertTrue(len(a) == len(a) + 1)

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						SCREAMING_SNAKE_CASE						= ids_tensor((self.batch_size, 1)		,					config.vocab_size)

						# append to next input_ids and
						SCREAMING_SNAKE_CASE						= torch.cat([input_ids, next_tokens]		,					dim=-1)

						SCREAMING_SNAKE_CASE						= model(a)['last_hidden_state']
						SCREAMING_SNAKE_CASE						= model(a		,					past_key_values=a)['last_hidden_state']

						# select random slice
						SCREAMING_SNAKE_CASE						= ids_tensor((1,)		,					output_from_past.shape[-1]).item()
						SCREAMING_SNAKE_CASE						= output_from_no_past[:, -1, random_slice_idx].detach()
						SCREAMING_SNAKE_CASE						= output_from_past[:, 0, random_slice_idx].detach()

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(a		,					a		,					atol=1E-3))







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					)      ->       str:
						SCREAMING_SNAKE_CASE						= UMTaModel(config=a).to(a).half().eval()
						SCREAMING_SNAKE_CASE						= model(**a)['last_hidden_state']
						self.parent.assertFalse(torch.isnan(a).any().item())



@require_torch
class 		_snake_case  (  A__   ,    A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    Any			     =							(
			    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
			)
			_lowercase		:    str			     =							(UMTaForConditionalGeneration,) if is_torch_available() else ()
			_lowercase		:    Tuple			     =							(
			    {
			        '''conversational''': UMTaForConditionalGeneration,
			        '''feature-extraction''': UMTaModel,
			        '''summarization''': UMTaForConditionalGeneration,
			        '''text2text-generation''': UMTaForConditionalGeneration,
			        '''translation''': UMTaForConditionalGeneration,
			        '''question-answering''': UMTaForQuestionAnswering,
			    }
			    if is_torch_available()
			    else {}
			)
			_lowercase		:    int			     =							True
			_lowercase		:    List[Any]			     =							False
			_lowercase		:    Union[str, Any]			     =							False
			_lowercase		:    int			     =							True
			_lowercase		:    Any			     =							True
			# The small UMT5 model needs higher percentages for CPU/MP tests
			_lowercase		:    int			     =							[0.8, 0.9]






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= UMTaModelTester(self)






			@unittest.skip('Test has a segmentation fault on torch 1.8.0')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE						= UMTaModel(config_and_inputs[0]).to(a)
						with tempfile.TemporaryDirectory() as tmpdirname:
									torch.onnx.export(
									    a		,					(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,					f'''{tmpdirname}/t5_test.onnx'''		,					export_params=a		,					opset_version=9		,					input_names=['input_ids', 'decoder_input_ids']		,					)






			@unittest.skipIf(torch_device == 'cpu'		,					'Cant do half precision')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE						= config_and_inputs[0]
						SCREAMING_SNAKE_CASE						= UMTaForConditionalGeneration(a).eval()
						model.to(a)

						SCREAMING_SNAKE_CASE						= {
						    'head_mask': torch.zeros(config.num_layers		,					config.num_heads		,					device=a),
						    'decoder_head_mask': torch.zeros(config.num_decoder_layers		,					config.num_heads		,					device=a),
						    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers		,					config.num_heads		,					device=a),
						}

						for attn_name, (name, mask) in zip(a		,					head_masking.items()):
									SCREAMING_SNAKE_CASE						= {name: mask}
									# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
									if name == "head_mask":
												SCREAMING_SNAKE_CASE						= torch.ones(
												    config.num_decoder_layers		,					config.num_heads		,					device=a)

									SCREAMING_SNAKE_CASE						= model.generate(
									    config_and_inputs[1]['input_ids']		,					num_beams=1		,					max_length=3		,					output_attentions=a		,					return_dict_in_generate=a		,					**a		,					)
									# We check the state of decoder_attentions and cross_attentions just from the last step
									SCREAMING_SNAKE_CASE						= out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
									self.assertEqual(sum([w.sum().item() for w in attn_weights])		,					0.0)







			@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 		_snake_case  (  unittest.TestCase					):






			@slow
			@unittest.skip(
			    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= UMTaForConditionalGeneration.from_pretrained('google/umt5-small'		,					return_dict=a).to(a)
						SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained('google/umt5-small'		,					use_fast=a		,					legacy=a)
						SCREAMING_SNAKE_CASE						= [
						    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
						    'No se como puedo <extra_id_0>.',
						    'This is the reason why we <extra_id_0> them.',
						    'The <extra_id_0> walks in <extra_id_1>, seats',
						    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
						]
						SCREAMING_SNAKE_CASE						= tokenizer(a		,					return_tensors='pt'		,					padding=a).input_ids
						# fmt: off
						SCREAMING_SNAKE_CASE						= torch.tensor(
						    [
						        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
						    ])
						# fmt: on
						torch.testing.assert_allclose(a		,					a)

						SCREAMING_SNAKE_CASE						= model.generate(input_ids.to(a))
						SCREAMING_SNAKE_CASE						= [
						    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
						    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
						]
						SCREAMING_SNAKE_CASE						= tokenizer.batch_decode(a)
						self.assertEqual(a		,					a)




