


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        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   PegasusConfig
					__SCREAMING_SNAKE_CASE       :  List[str]						=   {}
					__SCREAMING_SNAKE_CASE       :  Tuple						=   'gelu'

					def __init__(     self       :   Any							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Optional[int]=13							,					UpperCamelCase__       :   Optional[Any]=7							,					UpperCamelCase__       :   List[str]=True							,					UpperCamelCase__       :   List[str]=False							,					UpperCamelCase__       :   int=99							,					UpperCamelCase__       :   int=32							,					UpperCamelCase__       :   str=2							,					UpperCamelCase__       :   List[Any]=4							,					UpperCamelCase__       :   Dict=37							,					UpperCamelCase__       :   Dict=0.1							,					UpperCamelCase__       :   List[Any]=0.1							,					UpperCamelCase__       :   str=40							,					UpperCamelCase__       :   Tuple=2							,					UpperCamelCase__       :   Any=1							,					UpperCamelCase__       :   Tuple=0							,					):






										'''simple docstring'''






										lowercase_  							=			parent
										lowercase_  							=			batch_size
										lowercase_  							=			seq_length
										lowercase_  							=			is_training
										lowercase_  							=			use_labels
										lowercase_  							=			vocab_size
										lowercase_  							=			hidden_size
										lowercase_  							=			num_hidden_layers
										lowercase_  							=			num_attention_heads
										lowercase_  							=			intermediate_size

										lowercase_  							=			hidden_dropout_prob
										lowercase_  							=			attention_probs_dropout_prob
										lowercase_  							=			max_position_embeddings
										lowercase_  							=			eos_token_id
										lowercase_  							=			pad_token_id
										lowercase_  							=			bos_token_id

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size       )
										lowercase_  							=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )							,					1       )
										lowercase_  							=			tf.concat([input_ids, eos_tensor]							,					axis=1       )

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

										lowercase_  							=			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							,					)
										lowercase_  							=			prepare_pegasus_inputs_dict(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )
										return config, inputs_dict







					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			TFPegasusModel(config=UpperCamelCase__       ).get_decoder()
										lowercase_  							=			inputs_dict["""input_ids"""]

										lowercase_  							=			input_ids[:1, :]
										lowercase_  							=			inputs_dict["""attention_mask"""][:1, :]
										lowercase_  							=			inputs_dict["""head_mask"""]
										lowercase_  							=			1

										# first forward pass
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					head_mask=UpperCamelCase__							,					use_cache=UpperCamelCase__       )

										lowercase_							,					lowercase_  							=			outputs.to_tuple()

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

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

										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__       )[0]
										lowercase_  							=			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
										lowercase_  							=			int(ids_tensor((1,)							,					output_from_past.shape[-1]       )       )
										lowercase_  							=			output_from_no_past[:, -3:, random_slice_idx]
										lowercase_  							=			output_from_past[:, :, random_slice_idx]

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



def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__=None						,							UpperCAmelCase__=None						,							UpperCAmelCase__=None						,							UpperCAmelCase__=None						,							UpperCAmelCase__=None						,							):
					if attention_mask is None:
										lowercase_  							=			tf.cast(tf.math.not_equal(UpperCAmelCase__						,							config.pad_token_id  )						,							tf.inta  )
					if decoder_attention_mask is None:
										lowercase_  							=			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:
										lowercase_  							=			tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
					if decoder_head_mask is None:
										lowercase_  							=			tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
					if cross_attn_head_mask is None:
										lowercase_  							=			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        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  int						=   (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
					__SCREAMING_SNAKE_CASE       :  Dict						=   (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
					__SCREAMING_SNAKE_CASE       :  Dict						=   (
					    {
					        'conversational': TFPegasusForConditionalGeneration,
					        'feature-extraction': TFPegasusModel,
					        'summarization': TFPegasusForConditionalGeneration,
					        'text2text-generation': TFPegasusForConditionalGeneration,
					        'translation': TFPegasusForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					__SCREAMING_SNAKE_CASE       :  Dict						=   True
					__SCREAMING_SNAKE_CASE       :  Dict						=   False
					__SCREAMING_SNAKE_CASE       :  Dict						=   False

					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			TFPegasusModelTester(self       )
										lowercase_  							=			ConfigTester(self							,					config_class=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										self.config_tester.run_common_tests()







					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			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        UpperCamelCase__   (	unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  List[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       :  Optional[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
					__SCREAMING_SNAKE_CASE       :  Union[str, Any]						=   'google/pegasus-xsum'

					@cached_property
					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										return AutoTokenizer.from_pretrained(self.model_name       )

					@cached_property
					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
										return model

					def   UpperCAmelCase__       (     self       :   Dict							,					**UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			self.translate_src_text(**UpperCamelCase__       )
										assert self.expected_text == generated_words

					def   UpperCAmelCase__       (     self       :   str							,					**UpperCamelCase__       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.tokenizer(self.src_text							,					**UpperCamelCase__							,					padding=UpperCamelCase__							,					return_tensors="""tf"""       )
										lowercase_  							=			self.model.generate(
										    model_inputs.input_ids							,					attention_mask=model_inputs.attention_mask							,					num_beams=2							,					use_cache=UpperCamelCase__							,					)
										lowercase_  							=			self.tokenizer.batch_decode(generated_ids.numpy()							,					skip_special_tokens=UpperCamelCase__       )
										return generated_words







					@slow
					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										self._assert_generated_batch_equal_expected()






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   ['pixel_values']

					def __init__(     self       :   List[Any]							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Union[int, float] = 1 / 255							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   int = 8							,					**UpperCamelCase__       :   Optional[Any]							,					):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )

										lowercase_  							=			do_rescale
										lowercase_  							=			rescale_factor
										lowercase_  							=			do_pad
										lowercase_  							=			pad_size

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										return rescale(UpperCamelCase__							,					scale=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			get_image_size(UpperCamelCase__       )
										lowercase_  							=			(old_height // size + 1) * size - old_height
										lowercase_  							=			(old_width // size + 1) * size - old_width

										return pad(UpperCamelCase__							,					((0, pad_height), (0, pad_width))							,					mode="""symmetric"""							,					data_format=UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[float] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[Union[str, TensorType]] = None							,					UpperCamelCase__       :   Union[str, ChannelDimension] = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






										lowercase_  							=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_  							=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_  							=			do_pad if do_pad is not None else self.do_pad
										lowercase_  							=			pad_size if pad_size is not None else self.pad_size

										lowercase_  							=			make_list_of_images(UpperCamelCase__       )

										if not valid_images(UpperCamelCase__       ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

										if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

										# All transformations expect numpy arrays.
										lowercase_  							=			[to_numpy_array(UpperCamelCase__       ) for image in images]

										if do_rescale:
															lowercase_  							=			[self.rescale(image=UpperCamelCase__							,					scale=UpperCamelCase__       ) for image in images]

										if do_pad:
															lowercase_  							=			[self.pad(UpperCamelCase__							,					size=UpperCamelCase__       ) for image in images]

										lowercase_  							=			[to_channel_dimension_format(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			{"""pixel_values""": images}
										return BatchFeature(data=UpperCamelCase__							,					tensor_type=UpperCamelCase__       )


