
'''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 			lowerCAmelCase :
		a							:					str       =					PegasusConfig
		a							:					List[str]       =					{}
		a							:					Dict       =					"""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					,						):
								_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     lowercase       (				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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_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 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Any=None	,			_SCREAMING_SNAKE_CASE		:   str=None	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=None	,			_SCREAMING_SNAKE_CASE		:   List[Any]=None	,			_SCREAMING_SNAKE_CASE		:   Dict=None	,			):
						if attention_mask is None:
												_SCREAMING_SNAKE_CASE				= tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE	,			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 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase		,     unittest.TestCase			):
		a							:					Tuple       =					(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
		a							:					int       =					(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
		a							:					Dict       =					(
		    {
		        """conversational""": TFPegasusForConditionalGeneration,
		        """feature-extraction""": TFPegasusModel,
		        """summarization""": TFPegasusForConditionalGeneration,
		        """text2text-generation""": TFPegasusForConditionalGeneration,
		        """translation""": TFPegasusForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		a							:					Union[str, Any]       =					True
		a							:					Tuple       =					False
		a							:					List[str]       =					False




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TFPegasusModelTester(self      )
								_SCREAMING_SNAKE_CASE				= ConfigTester(self					,						config_class=UpperCamelCase      )




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




		def     lowercase       (				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 			lowerCAmelCase (				unittest.TestCase			):
		a							:					List[str]       =					[
		    """ 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!\" """,
		]
		a							:					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
		a							:					Optional[int]       =					"""google/pegasus-xsum"""




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




		@cached_property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
								return model




		def     lowercase       (				self					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.translate_src_text(**UpperCamelCase      )
								assert self.expected_text == generated_words




		def     lowercase       (				self					,						**UpperCamelCase      ):
								_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     lowercase       (				self      ):
								self._assert_generated_batch_equal_expected()

'''simple docstring'''







from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
						import torch

if is_tf_available():
						import tensorflow as tf


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor"""]
		a							:					Tuple       =					"""SamImageProcessor"""




		def __init__(				self					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.image_processor
								_SCREAMING_SNAKE_CASE				= -10
								_SCREAMING_SNAKE_CASE				= self.image_processor.size["longest_edge"]




		def __call__(				self					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= self.image_processor(
								    UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)

								# pop arguments that are not used in the foward but used nevertheless
								_SCREAMING_SNAKE_CASE				= encoding_image_processor["original_sizes"]

								if hasattr(UpperCamelCase					,						"numpy"      ):  # Checks if Torch or TF tensor
														_SCREAMING_SNAKE_CASE				= original_sizes.numpy()

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._check_and_preprocess_points(
								    input_points=UpperCamelCase					,						input_labels=UpperCamelCase					,						input_boxes=UpperCamelCase					,						)

								_SCREAMING_SNAKE_CASE				= self._normalize_and_convert(
								    UpperCamelCase					,						UpperCamelCase					,						input_points=UpperCamelCase					,						input_labels=UpperCamelCase					,						input_boxes=UpperCamelCase					,						return_tensors=UpperCamelCase					,						)

								return encoding_image_processor




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase="pt"					,						):
								if input_points is not None:
														if len(UpperCamelCase      ) != len(UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						original_sizes[0]      ) for point in input_points
																				]
														else:
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						UpperCamelCase      )
																				    for point, original_size in zip(UpperCamelCase					,						UpperCamelCase      )
																				]
														# check that all arrays have the same shape
														if not all(point.shape == input_points[0].shape for point in input_points      ):
																				if input_labels is not None:
																										_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._pad_points_and_labels(UpperCamelCase					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_labels is not None:
														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_boxes is not None:
														if len(UpperCamelCase      ) != len(UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						original_sizes[0]					,						is_bounding_box=UpperCamelCase      )
																				    for box in input_boxes
																				]
														else:
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						UpperCamelCase					,						is_bounding_box=UpperCamelCase      )
																				    for box, original_size in zip(UpperCamelCase					,						UpperCamelCase      )
																				]
														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_boxes is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# boxes batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_boxes.unsqueeze(1      ) if len(input_boxes.shape      ) != 3 else input_boxes
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# boxes batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_boxes.shape      ) != 3 else input_boxes
														encoding_image_processor.update({"input_boxes": input_boxes}      )
								if input_points is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_points.unsqueeze(1      ) if len(input_points.shape      ) != 4 else input_points
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_points.shape      ) != 4 else input_points
														encoding_image_processor.update({"input_points": input_points}      )
								if input_labels is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_labels.unsqueeze(1      ) if len(input_labels.shape      ) != 3 else input_labels
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_labels.shape      ) != 3 else input_labels
														encoding_image_processor.update({"input_labels": input_labels}      )

								return encoding_image_processor




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= max([point.shape[0] for point in input_points]      )
								_SCREAMING_SNAKE_CASE				= []
								for i, point in enumerate(UpperCamelCase      ):
														if point.shape[0] != expected_nb_points:
																				_SCREAMING_SNAKE_CASE				= np.concatenate(
																				    [point, np.zeros((expected_nb_points - point.shape[0], 2)      ) + self.point_pad_value]					,						axis=0      )
																				_SCREAMING_SNAKE_CASE				= np.append(input_labels[i]					,						[self.point_pad_value]      )
														processed_input_points.append(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= processed_input_points
								return input_points, input_labels




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=False      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= original_size
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.image_processor._get_preprocess_shape(UpperCamelCase					,						longest_edge=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= deepcopy(UpperCamelCase      ).astype(UpperCamelCase      )

								if is_bounding_box:
														_SCREAMING_SNAKE_CASE				= coords.reshape(-1					,						2					,						2      )

								_SCREAMING_SNAKE_CASE				= coords[..., 0] * (new_w / old_w)
								_SCREAMING_SNAKE_CASE				= coords[..., 1] * (new_h / old_h)

								if is_bounding_box:
														_SCREAMING_SNAKE_CASE				= coords.reshape(-1					,						4      )

								return coords




		def     lowercase       (				self					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						):
								if input_points is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):  # Checks for TF or Torch tensor
																				_SCREAMING_SNAKE_CASE				= input_points.numpy().tolist()

														if not isinstance(UpperCamelCase					,						UpperCamelCase      ) or not isinstance(input_points[0]					,						UpperCamelCase      ):
																				raise ValueError("Input points must be a list of list of floating points."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ) for input_point in input_points]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if input_labels is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):
																				_SCREAMING_SNAKE_CASE				= input_labels.numpy().tolist()

														if not isinstance(UpperCamelCase					,						UpperCamelCase      ) or not isinstance(input_labels[0]					,						UpperCamelCase      ):
																				raise ValueError("Input labels must be a list of list integers."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ) for label in input_labels]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if input_boxes is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):
																				_SCREAMING_SNAKE_CASE				= input_boxes.numpy().tolist()

														if (
														    not isinstance(UpperCamelCase					,						UpperCamelCase      )
														    or not isinstance(input_boxes[0]					,						UpperCamelCase      )
														    or not isinstance(input_boxes[0][0]					,						UpperCamelCase      )
														):
																				raise ValueError("Input boxes must be a list of list of list of floating points."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ).astype(np.floataa      ) for box in input_boxes]
								else:
														_SCREAMING_SNAKE_CASE				= None

								return input_points, input_labels, input_boxes




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(UpperCamelCase      )      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.image_processor.post_process_masks(*UpperCamelCase					,						**UpperCamelCase      )