
"""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 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  Optional[int]						   =		PegasusConfig
		__lowercase							:  List[str]						   =		{}
		__lowercase							:  Dict						   =		'gelu'




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__=1_3							,  lowerCAmelCase__=7							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  lowerCAmelCase__=9_9							,  lowerCAmelCase__=3_2							,  lowerCAmelCase__=2							,  lowerCAmelCase__=4							,  lowerCAmelCase__=3_7							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=4_0							,  lowerCAmelCase__=2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0							,  ):
				__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 	snake_case_     (     self):
				__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(__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase)
				return config, inputs_dict




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = TFPegasusModel(config=__UpperCAmelCase).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(__UpperCAmelCase							,  attention_mask=__UpperCAmelCase							,  head_mask=__UpperCAmelCase							,  use_cache=__UpperCAmelCase)

				__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(__UpperCAmelCase							,  attention_mask=__UpperCAmelCase)[0]
				__SCREAMING_SNAKE_CASE            = 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
				__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(__UpperCAmelCase							,  __UpperCAmelCase							,  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 						SCREAMING_SNAKE_CASE_ (						__A     ,				__A     ,				unittest.TestCase ):




		"""simple docstring"""





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




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = TFPegasusModelTester(self)
				__SCREAMING_SNAKE_CASE            = ConfigTester(self							,  config_class=__UpperCAmelCase)




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




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = 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 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





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




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




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




		def 	snake_case_     (     self							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.translate_src_text(**__UpperCAmelCase)
				assert self.expected_text == generated_words




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




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





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Union[str, Any]  							=						'transfo-xl'
						lowerCAmelCase__     :  Any  							=						['mems']
						lowerCAmelCase__     :  Tuple  							=						{
						    'n_token': 'vocab_size',
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}





						def __init__(				self						,__UpperCAmelCase=26_77_35						,__UpperCAmelCase=[2_00_00, 4_00_00, 20_00_00]						,__UpperCAmelCase=10_24						,__UpperCAmelCase=10_24						,__UpperCAmelCase=16						,__UpperCAmelCase=64						,__UpperCAmelCase=40_96						,__UpperCAmelCase=4						,__UpperCAmelCase=False						,__UpperCAmelCase=18						,__UpperCAmelCase=16_00						,__UpperCAmelCase=10_00						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=0						,__UpperCAmelCase=-1						,__UpperCAmelCase=True						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0						,__UpperCAmelCase=True						,__UpperCAmelCase="normal"						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=1e-5						,__UpperCAmelCase=0						,**__UpperCAmelCase						,)    -> Tuple:
													A__		   =							vocab_size
													A__		   =							[]
													self.cutoffs.extend(__UpperCAmelCase   )
													if proj_share_all_but_first:
																				A__		   =							[False] + [True] * len(self.cutoffs   )
													else:
																				A__		   =							[False] + [False] * len(self.cutoffs   )
													A__		   =							d_model
													A__		   =							d_embed
													A__		   =							d_head
													A__		   =							d_inner
													A__		   =							div_val
													A__		   =							pre_lnorm
													A__		   =							n_layer
													A__		   =							n_head
													A__		   =							mem_len
													A__		   =							same_length
													A__		   =							attn_type
													A__		   =							clamp_len
													A__		   =							sample_softmax
													A__		   =							adaptive
													A__		   =							dropout
													A__		   =							dropatt
													A__		   =							untie_r
													A__		   =							init
													A__		   =							init_range
													A__		   =							proj_init_std
													A__		   =							init_std
													A__		   =							layer_norm_epsilon
													super().__init__(eos_token_id=__UpperCAmelCase						,**__UpperCAmelCase   )





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													# Message copied from Transformer-XL documentation
													logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
													return -1





						@max_position_embeddings.setter
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> int:
													# Message copied from Transformer-XL documentation
													raise NotImplementedError(
													    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )





