
"""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 	_lowercase       :
	lowercase_				=     PegasusConfig
	lowercase_				=     {}
	lowercase_				=     'gelu'




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

			lowerCamelCase					:      List[Any]          =   hidden_dropout_prob
			lowerCamelCase					:      Tuple          =   attention_probs_dropout_prob
			lowerCamelCase					:      List[Any]          =   max_position_embeddings
			lowerCamelCase					:      List[str]          =   eos_token_id
			lowerCamelCase					:      Any          =   pad_token_id
			lowerCamelCase					:      Tuple          =   bos_token_id




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      List[Any]          =   ids_tensor([self.batch_size, self.seq_length - 1]		,			self.vocab_size )
			lowerCamelCase					:      List[Any]          =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )		,			1 )
			lowerCamelCase					:      Optional[int]          =   tf.concat([input_ids, eos_tensor]		,			axis=1 )

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

			lowerCamelCase					:      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		,			)
			lowerCamelCase					:      Dict          =   prepare_pegasus_inputs_dict(__a		,			__a		,			__a )
			return config, inputs_dict




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      List[str]          =   TFPegasusModel(config=__a ).get_decoder()
			lowerCamelCase					:      Union[str, Any]          =   inputs_dict['input_ids']

			lowerCamelCase					:      Optional[Any]          =   input_ids[:1, :]
			lowerCamelCase					:      int          =   inputs_dict['attention_mask'][:1, :]
			lowerCamelCase					:      Optional[Any]          =   inputs_dict['head_mask']
			lowerCamelCase					:      str          =   1

			# first forward pass
			lowerCamelCase					:      Tuple          =   model(__a		,			attention_mask=__a		,			head_mask=__a		,			use_cache=__a )

			lowerCamelCase					:      Dict          =   outputs.to_tuple()

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

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

			lowerCamelCase					:      Union[str, Any]          =   model(__a		,			attention_mask=__a )[0]
			lowerCamelCase					:      Tuple          =   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
			lowerCamelCase					:      str          =   int(ids_tensor((1,)		,			output_from_past.shape[-1] ) )
			lowerCamelCase					:      Optional[int]          =   output_from_no_past[:, -3:, random_slice_idx]
			lowerCamelCase					:      Tuple          =   output_from_past[:, :, random_slice_idx]

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


def 		UpperCAmelCase (		a_,						a_,						a_,						a_=None,						a_=None,						a_=None,						a_=None,						a_=None,						):





		'''simple docstring'''


		if attention_mask is None:
				lowerCamelCase					:      List[Any]          =   tf.cast(tf.math.not_equal(_UpperCAmelCase,						config.pad_token_id      ),						tf.inta      )
		if decoder_attention_mask is None:
				lowerCamelCase					:      str          =   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:
				lowerCamelCase					:      Tuple          =   tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
		if decoder_head_mask is None:
				lowerCamelCase					:      Optional[Any]          =   tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
		if cross_attn_head_mask is None:
				lowerCamelCase					:      Any          =   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       (    SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
	lowercase_				=     (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
	lowercase_				=     (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
	lowercase_				=     (
	    {
	        'conversational': TFPegasusForConditionalGeneration,
	        'feature-extraction': TFPegasusModel,
	        'summarization': TFPegasusForConditionalGeneration,
	        'text2text-generation': TFPegasusForConditionalGeneration,
	        'translation': TFPegasusForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowercase_				=     True
	lowercase_				=     False
	lowercase_				=     False




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      List[Any]          =   TFPegasusModelTester(self )
			lowerCamelCase					:      Dict          =   ConfigTester(self		,			config_class=__a )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			self.config_tester.run_common_tests()




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      List[str]          =   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 	_lowercase       (    unittest.TestCase		):
	lowercase_				=     [
	    ' 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_				=     [
	    '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_				=     'google/pegasus-xsum'




	@cached_property
	def       _UpperCamelCase   (			self )		->							str:
			return AutoTokenizer.from_pretrained(self.model_name )




	@cached_property
	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      str          =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
			return model




	def       _UpperCamelCase   (			self		,			**UpperCAmelCase_ )		->							Any:
			lowerCamelCase					:      Dict          =   self.translate_src_text(**__a )
			assert self.expected_text == generated_words




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




	@slow
	def       _UpperCamelCase   (			self )		->							str:
			self._assert_generated_batch_equal_expected()








"""simple docstring"""

import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
					import ctypes
					import msvcrt  # noqa






					class 	_lowercase       (    ctypes.Structure		):
						# _fields is a specific attr expected by ctypes
						lowercase_				=     [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def 		UpperCAmelCase (		):





		'''simple docstring'''


		if os.name == "nt":
				lowerCamelCase					:      Optional[int]          =   CursorInfo()
				lowerCamelCase					:      Union[str, Any]          =   ctypes.windll.kernelaa.GetStdHandle(-11      )
				ctypes.windll.kernelaa.GetConsoleCursorInfo(a_,						ctypes.byref(a_      )      )
				lowerCamelCase					:      Dict          =   False
				ctypes.windll.kernelaa.SetConsoleCursorInfo(a_,						ctypes.byref(a_      )      )
		elif os.name == "posix":
				sys.stdout.write('\033[?25l'      )
				sys.stdout.flush()
def 		UpperCAmelCase (		):





		'''simple docstring'''


		if os.name == "nt":
				lowerCamelCase					:      List[str]          =   CursorInfo()
				lowerCamelCase					:      List[Any]          =   ctypes.windll.kernelaa.GetStdHandle(-11      )
				ctypes.windll.kernelaa.GetConsoleCursorInfo(a_,						ctypes.byref(a_      )      )
				lowerCamelCase					:      Optional[Any]          =   True
				ctypes.windll.kernelaa.SetConsoleCursorInfo(a_,						ctypes.byref(a_      )      )
		elif os.name == "posix":
				sys.stdout.write('\033[?25h'      )
				sys.stdout.flush()





@contextmanager
def 		UpperCAmelCase (		):





		'''simple docstring'''


		try:
				hide_cursor()
				yield
		finally:
				show_cursor()






