






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     lowerCamelCase :







		'''simple docstring'''



		_snake_case       :       List[str]					=    PegasusConfig
		_snake_case       :       Optional[Any]					=    {}
		_snake_case       :       Optional[Any]					=    '''gelu'''



		def __init__(				self							,						_UpperCamelCase							,						_UpperCamelCase=1_3							,						_UpperCamelCase=7							,						_UpperCamelCase=True							,						_UpperCamelCase=False							,						_UpperCamelCase=9_9							,						_UpperCamelCase=3_2							,						_UpperCamelCase=2							,						_UpperCamelCase=4							,						_UpperCamelCase=3_7							,						_UpperCamelCase=0.1							,						_UpperCamelCase=0.1							,						_UpperCamelCase=4_0							,						_UpperCamelCase=2							,						_UpperCamelCase=1							,						_UpperCamelCase=0							,						)      ->				List[Any]:
							UpperCAmelCase_       : int				=		parent
							UpperCAmelCase_       : List[str]				=		batch_size
							UpperCAmelCase_       : int				=		seq_length
							UpperCAmelCase_       : str				=		is_training
							UpperCAmelCase_       : Dict				=		use_labels
							UpperCAmelCase_       : Tuple				=		vocab_size
							UpperCAmelCase_       : Union[str, Any]				=		hidden_size
							UpperCAmelCase_       : str				=		num_hidden_layers
							UpperCAmelCase_       : Dict				=		num_attention_heads
							UpperCAmelCase_       : Tuple				=		intermediate_size

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



		def    __UpperCAmelCase     (				self  )      ->				Tuple:
							UpperCAmelCase_       : str				=		ids_tensor([self.batch_size, self.seq_length - 1]							,						self.vocab_size  )
							UpperCAmelCase_       : Tuple				=		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_       : Any				=		ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size  )

							UpperCAmelCase_       : Dict				=		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_       : str				=		prepare_pegasus_inputs_dict(_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )
							return config, inputs_dict



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				str:
							UpperCAmelCase_       : Dict				=		TFPegasusModel(config=_UpperCamelCase  ).get_decoder()
							UpperCAmelCase_       : str				=		inputs_dict['input_ids']

							UpperCAmelCase_       : Any				=		input_ids[:1, :]
							UpperCAmelCase_       : Optional[Any]				=		inputs_dict['attention_mask'][:1, :]
							UpperCAmelCase_       : Union[str, Any]				=		inputs_dict['head_mask']
							UpperCAmelCase_       : Union[str, Any]				=		1

							# first forward pass
							UpperCAmelCase_       : str				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase							,						head_mask=_UpperCamelCase							,						use_cache=_UpperCamelCase  )

							UpperCAmelCase_			,    UpperCAmelCase_       : str				=		outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							UpperCAmelCase_       : List[str]				=		ids_tensor((self.batch_size, 3)							,						config.vocab_size  )
							UpperCAmelCase_       : Union[str, Any]				=		tf.cast(ids_tensor((self.batch_size, 3)							,						2  )							,						tf.inta  )

							# append to next input_ids and
							UpperCAmelCase_       : Union[str, Any]				=		tf.concat([input_ids, next_tokens]							,						axis=-1  )
							UpperCAmelCase_       : Optional[Any]				=		tf.concat([attention_mask, next_attn_mask]							,						axis=-1  )

							UpperCAmelCase_       : Any				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase  )[0]
							UpperCAmelCase_       : Tuple				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase							,						past_key_values=_UpperCamelCase  )[0]

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

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

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





def 	lowercase__ (	__snake_case       :				str	,			__snake_case       :				Optional[int]	,			__snake_case       :				int	,			__snake_case       :				Dict=None	,			__snake_case       :				Optional[Any]=None	,			__snake_case       :				List[str]=None	,			__snake_case       :				str=None	,			__snake_case       :				Any=None	,			):


					'''simple docstring'''


					if attention_mask is None:
										UpperCAmelCase_       : int				=		tf.cast(tf.math.not_equal(UpperCamelCase__	,			config.pad_token_id    )	,			tf.inta    )
					if decoder_attention_mask is None:
										UpperCAmelCase_       : Dict				=		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_       : Union[str, Any]				=		tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
					if decoder_head_mask is None:
										UpperCAmelCase_       : Any				=		tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
					if cross_attn_head_mask is None:
										UpperCAmelCase_       : List[str]				=		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     lowerCamelCase (lowercase_					,    lowercase_					,    unittest.TestCase     ):







		'''simple docstring'''



		_snake_case       :       str					=    (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
		_snake_case       :       Union[str, Any]					=    (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
		_snake_case       :       Optional[int]					=    (
		    {
		        '''conversational''': TFPegasusForConditionalGeneration,
		        '''feature-extraction''': TFPegasusModel,
		        '''summarization''': TFPegasusForConditionalGeneration,
		        '''text2text-generation''': TFPegasusForConditionalGeneration,
		        '''translation''': TFPegasusForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		_snake_case       :       Optional[Any]					=    True
		_snake_case       :       Optional[Any]					=    False
		_snake_case       :       List[str]					=    False



		def    __UpperCAmelCase     (				self  )      ->				List[str]:
							UpperCAmelCase_       : Union[str, Any]				=		TFPegasusModelTester(self  )
							UpperCAmelCase_       : str				=		ConfigTester(self							,						config_class=_UpperCamelCase  )



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



		def    __UpperCAmelCase     (				self  )      ->				List[Any]:
							UpperCAmelCase_       : Optional[Any]				=		self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase  )





@require_sentencepiece
@require_tokenizers
@require_tf
class     lowerCamelCase (unittest.TestCase     ):







		'''simple docstring'''



		_snake_case       :       Optional[Any]					=    [
		    ''' 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!" ''',
		]
		_snake_case       :       Union[str, 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
		_snake_case       :       Optional[int]					=    '''google/pegasus-xsum'''



		@cached_property
		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							return AutoTokenizer.from_pretrained(self.model_name  )



		@cached_property
		def    __UpperCAmelCase     (				self  )      ->				Dict:
							UpperCAmelCase_       : Optional[int]				=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
							return model



		def    __UpperCAmelCase     (				self							,						**_UpperCamelCase  )      ->				Union[str, Any]:
							UpperCAmelCase_       : Union[str, Any]				=		self.translate_src_text(**_UpperCamelCase  )
							assert self.expected_text == generated_words



		def    __UpperCAmelCase     (				self							,						**_UpperCamelCase  )      ->				Any:
							UpperCAmelCase_       : Optional[Any]				=		self.tokenizer(self.src_text							,						**_UpperCamelCase							,						padding=_UpperCamelCase							,						return_tensors='tf'  )
							UpperCAmelCase_       : Union[str, Any]				=		self.model.generate(
							    model_inputs.input_ids							,						attention_mask=model_inputs.attention_mask							,						num_beams=2							,						use_cache=_UpperCamelCase							,						)
							UpperCAmelCase_       : Optional[int]				=		self.tokenizer.batch_decode(generated_ids.numpy()							,						skip_special_tokens=_UpperCamelCase  )
							return generated_words



		@slow
		def    __UpperCAmelCase     (				self  )      ->				Optional[Any]:
							self._assert_generated_batch_equal_expected()









import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       tempfile.mkdtemp()

											snake_case_     							=       BlipImageProcessor()
											snake_case_     							=       GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'			)

											snake_case_     							=       BlipaProcessor(snake_case ,  snake_case			)

											processor.save_pretrained(self.tmpdirname			)




				def        a				(   self ,  **snake_case			):
											return AutoProcessor.from_pretrained(self.tmpdirname ,  **snake_case			).tokenizer




				def        a				(   self ,  **snake_case			):
											return AutoProcessor.from_pretrained(self.tmpdirname ,  **snake_case			).image_processor




				def        a				(   self			):
											shutil.rmtree(self.tmpdirname			)




				def        a				(   self			):
											snake_case_     							=       [np.random.randint(255 ,  size=(3, 30, 400) ,  dtype=np.uinta			)]

											snake_case_     							=       [Image.fromarray(np.moveaxis(snake_case ,  0 ,  -1			)			) for x in image_inputs]

											return image_inputs




				def        a				(   self			):
											snake_case_     							=       BlipaProcessor(tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor()			)
											processor.save_pretrained(self.tmpdirname			)

											snake_case_     							=       self.get_tokenizer(bos_token='(BOS)' ,  eos_token='(EOS)'			)
											snake_case_     							=       self.get_image_processor(do_normalize=snake_case ,  padding_value=1.0			)

											snake_case_     							=       BlipaProcessor.from_pretrained(
											    self.tmpdirname ,  bos_token='(BOS)' ,  eos_token='(EOS)' ,  do_normalize=snake_case ,  padding_value=1.0			)

											self.assertEqual(processor.tokenizer.get_vocab() ,  tokenizer_add_kwargs.get_vocab()			)
											self.assertIsInstance(processor.tokenizer ,  snake_case			)

											self.assertEqual(processor.image_processor.to_json_string() ,  image_processor_add_kwargs.to_json_string()			)
											self.assertIsInstance(processor.image_processor ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       image_processor(snake_case ,  return_tensors='np'			)
											snake_case_     							=       processor(images=snake_case ,  return_tensors='np'			)

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum() ,  input_processor[key].sum() ,  delta=1e-2			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'

											snake_case_     							=       processor(text=snake_case			)

											snake_case_     							=       tokenizer(snake_case ,  return_token_type_ids=snake_case			)

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key] ,  encoded_processor[key]			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'
											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       processor(text=snake_case ,  images=snake_case			)

											self.assertListEqual(list(inputs.keys()			) ,  ['pixel_values', 'input_ids', 'attention_mask']			)

											# test if it raises when no input is passed
											with pytest.raises(snake_case			):
																		processor()




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case_     							=       processor.batch_decode(snake_case			)
											snake_case_     							=       tokenizer.batch_decode(snake_case			)

											self.assertListEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'
											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       processor(text=snake_case ,  images=snake_case			)

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys()			) ,  ['pixel_values', 'input_ids', 'attention_mask']			)




