

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




				def __init__(   self ,  snake_case ,  snake_case=13 ,  snake_case=7 ,  snake_case=True ,  snake_case=False ,  snake_case=99 ,  snake_case=32 ,  snake_case=2 ,  snake_case=4 ,  snake_case=37 ,  snake_case=0.1 ,  snake_case=0.1 ,  snake_case=40 ,  snake_case=2 ,  snake_case=1 ,  snake_case=0 ,  ):
											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




				def        a				(   self			):
											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 ,  **self.config_updates ,  )
											snake_case_     							=       prepare_pegasus_inputs_dict(snake_case ,  snake_case ,  snake_case			)
											return config, inputs_dict




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       TFPegasusModel(config=snake_case			).get_decoder()
											snake_case_     							=       inputs_dict['input_ids']

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

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

											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(snake_case ,  attention_mask=snake_case			)[0]
											snake_case_     							=       model(snake_case ,  attention_mask=snake_case ,  past_key_values=snake_case			)[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(snake_case ,  snake_case ,  rtol=1e-3			)





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				):







							'''simple docstring'''






							if attention_mask is None:
														snake_case_     							=       tf.cast(tf.math.not_equal(UpperCamelCase__  ,				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) )
							if cross_attn_head_mask is None:
														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 					lowercase							(			lowercase_      ,    lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					str			     =     (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     (
				    {
				        '''conversational''': TFPegasusForConditionalGeneration,
				        '''feature-extraction''': TFPegasusModel,
				        '''summarization''': TFPegasusForConditionalGeneration,
				        '''text2text-generation''': TFPegasusForConditionalGeneration,
				        '''translation''': TFPegasusForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     True
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     False
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     False




				def        a				(   self			):
											snake_case_     							=       TFPegasusModelTester(self			)
											snake_case_     							=       ConfigTester(self ,  config_class=snake_case			)




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




				def        a				(   self			):
											snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*snake_case			)







@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowercase							(			unittest.TestCase     ):
				__SCREAMING_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!" ''',
				]
				__SCREAMING_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
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     '''google/pegasus-xsum'''




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




				@cached_property
				def        a				(   self			):
											snake_case_     							=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
											return model




				def        a				(   self ,  **snake_case			):
											snake_case_     							=       self.translate_src_text(**snake_case			)
											assert self.expected_text == generated_words




				def        a				(   self ,  **snake_case			):
											snake_case_     							=       self.tokenizer(self.src_text ,  **snake_case ,  padding=snake_case ,  return_tensors='tf'			)
											snake_case_     							=       self.model.generate(
											    model_inputs.input_ids ,  attention_mask=model_inputs.attention_mask ,  num_beams=2 ,  use_cache=snake_case ,  )
											snake_case_     							=       self.tokenizer.batch_decode(generated_ids.numpy() ,  skip_special_tokens=snake_case			)
											return generated_words




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







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




				def __call__(   self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  **snake_case			):

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




