



"""simple docstring"""







from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

A    =			logging.get_logger(__name__)






@add_end_docstrings(UpperCamelCase     )
class      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    List[str]      ,							**snake_case  :    str				)			->						Tuple:






									'''simple docstring'''

									super().__init__(**_lowerCAmelCase				)
									requires_backends(self      ,							'vision'				)
									requires_backends(self      ,							'torch'				)

									if self.framework != "pt":
																raise ValueError(f"""The {self.__class__} is only available in PyTorch."""				)

									self.check_model_type(_lowerCAmelCase				)



		def 					A_		( self  :    Any      ,							**snake_case  :    List[str]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					{}
									A         =					{}
									A         =					{}
									# preprocess args
									if "points_per_batch" in kwargs:
																A         =					kwargs['points_per_batch']
									if "points_per_crop" in kwargs:
																A         =					kwargs['points_per_crop']
									if "crops_n_layers" in kwargs:
																A         =					kwargs['crops_n_layers']
									if "crop_overlap_ratio" in kwargs:
																A         =					kwargs['crop_overlap_ratio']
									if "crop_n_points_downscale_factor" in kwargs:
																A         =					kwargs['crop_n_points_downscale_factor']
									# postprocess args
									if "pred_iou_thresh" in kwargs:
																A         =					kwargs['pred_iou_thresh']
									if "stability_score_offset" in kwargs:
																A         =					kwargs['stability_score_offset']
									if "mask_threshold" in kwargs:
																A         =					kwargs['mask_threshold']
									if "stability_score_thresh" in kwargs:
																A         =					kwargs['stability_score_thresh']
									if "crops_nms_thresh" in kwargs:
																A         =					kwargs['crops_nms_thresh']
									if "output_rle_mask" in kwargs:
																A         =					kwargs['output_rle_mask']
									if "output_bboxes_mask" in kwargs:
																A         =					kwargs['output_bboxes_mask']
									return preprocess_kwargs, forward_params, postprocess_kwargs



		def __call__( self  :    Optional[int]      ,							snake_case  :    Union[str, Any]      ,							*snake_case  :    Any      ,							snake_case  :    Union[str, Any]=None      ,							snake_case  :    Tuple=None      ,							**snake_case  :    str				)			->						Tuple:






									'''simple docstring'''

									return super().__call__(_lowerCAmelCase      ,							*_lowerCAmelCase      ,							num_workers=_lowerCAmelCase      ,							batch_size=_lowerCAmelCase      ,							**_lowerCAmelCase				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    int      ,							snake_case  :    Optional[int]=64      ,							snake_case  :    int = 0      ,							snake_case  :    str = 512 / 1_500      ,							snake_case  :    List[Any] = 32      ,							snake_case  :    List[str] = 1      ,							)			->						str:






									'''simple docstring'''

									A         =					load_image(_lowerCAmelCase				)
									A         =					self.image_processor.size['longest_edge']
									A      ,  A      ,  A      ,  A         =					self.image_processor.generate_crop_boxes(
									    _lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase				)
									A         =					self.image_processor(images=_lowerCAmelCase      ,							return_tensors='pt'				)

									with self.device_placement():
																if self.framework == "pt":
																							A         =					self.get_inference_context()
																							with inference_context():
																														A         =					self._ensure_tensor_on_device(_lowerCAmelCase      ,							device=self.device				)
																														A         =					self.model.get_image_embeddings(model_inputs.pop('pixel_values'				)				)
																														A         =					image_embeddings

									A         =					grid_points.shape[1]
									A         =					points_per_batch if points_per_batch is not None else n_points

									if points_per_batch <= 0:
																raise ValueError(
																    'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
																    'To return all points at once, set points_per_batch to None'				)

									for i in range(0      ,							_lowerCAmelCase      ,							_lowerCAmelCase				):
																A         =					grid_points[:, i : i + points_per_batch, :, :]
																A         =					input_labels[:, i : i + points_per_batch]
																A         =					i == n_points - points_per_batch
																yield {
																    "input_points": batched_points,
																    "input_labels": labels,
																    "input_boxes": crop_boxes,
																    "is_last": is_last,
																    **model_inputs,
																}



		def 					A_		( self  :    str      ,							snake_case  :    int      ,							snake_case  :    List[Any]=0.88      ,							snake_case  :    Tuple=0.95      ,							snake_case  :    Optional[int]=0      ,							snake_case  :    int=1      ,							)			->						List[Any]:






									'''simple docstring'''

									A         =					model_inputs.pop('input_boxes'				)
									A         =					model_inputs.pop('is_last'				)
									A         =					model_inputs.pop('original_sizes'				).tolist()
									A         =					model_inputs.pop('reshaped_input_sizes'				).tolist()

									A         =					self.model(**_lowerCAmelCase				)

									# post processing happens here in order to avoid CPU GPU copies of ALL the masks
									A         =					model_outputs['pred_masks']
									A         =					self.image_processor.post_process_masks(
									    _lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							binarize=_lowerCAmelCase				)
									A         =					model_outputs['iou_scores']
									A      ,  A      ,  A         =					self.image_processor.filter_masks(
									    masks[0]      ,							iou_scores[0]      ,							original_sizes[0]      ,							input_boxes[0]      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							)
									return {
									    "masks": masks,
									    "is_last": is_last,
									    "boxes": boxes,
									    "iou_scores": iou_scores,
									}



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    List[Any]      ,							snake_case  :    str=False      ,							snake_case  :    str=False      ,							snake_case  :    List[Any]=0.7      ,							)			->						int:






									'''simple docstring'''

									A         =					[]
									A         =					[]
									A         =					[]
									for model_output in model_outputs:
																all_scores.append(model_output.pop('iou_scores'				)				)
																all_masks.extend(model_output.pop('masks'				)				)
																all_boxes.append(model_output.pop('boxes'				)				)

									A         =					torch.cat(_lowerCAmelCase				)
									A         =					torch.cat(_lowerCAmelCase				)
									A      ,  A      ,  A      ,  A         =					self.image_processor.post_process_for_mask_generation(
									    _lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase      ,							_lowerCAmelCase				)

									A         =					defaultdict(_lowerCAmelCase				)
									for output in model_outputs:
																for k, v in output.items():
																							extra[k].append(_lowerCAmelCase				)

									A         =					{}
									if output_rle_mask:
																A         =					rle_mask

									if output_bboxes_mask:
																A         =					bounding_boxes

									return {"masks": output_masks, "scores": iou_scores, **optional, **extra}








"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


A    =			['bert-base-uncased', 'bert-base-cased']
A    =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():






							class      UpperCAmelCase__		(      tf.keras.Model     ):



									def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]				)			->						List[str]:






																'''simple docstring'''

																super().__init__()
																A         =					tokenizer
																A         =					AutoConfig.from_pretrained(snake_case				)
																A         =					TFAutoModel.from_config(snake_case				)



									def 					A_		( self  :    Any      ,							snake_case  :    Optional[int]				)			->						Dict:






																'''simple docstring'''

																A         =					self.tokenizer(snake_case				)
																A         =					self.bert(**snake_case				)
																return out["pooler_output"]






@require_tf
@require_tensorflow_text
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    int				)			->						Tuple:






									'''simple docstring'''

									super().setUp()

									A         =					[
									    BertTokenizer.from_pretrained(snake_case				) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
									]  # repeat for when fast_bert_tokenizer=false
									A         =					[TFBertTokenizer.from_pretrained(snake_case				) for checkpoint in TOKENIZER_CHECKPOINTS] + [
									    TFBertTokenizer.from_pretrained(snake_case      ,							use_fast_bert_tokenizer=snake_case				)
									    for checkpoint in TOKENIZER_CHECKPOINTS
									]
									assert len(self.tokenizers				) == len(self.tf_tokenizers				)

									A         =					[
									    'This is a straightforward English test sentence.',
									    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
									    'Now we\'re going to add some Chinese: 一 二 三 一二三',
									    'And some much more rare Chinese: 齉 堃 齉堃',
									    'Je vais aussi écrire en français pour tester les accents',
									    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
									]
									A         =					list(zip(self.test_sentences      ,							self.test_sentences[::-1]				)				)



		def 					A_		( self  :    List[Any]				)			->						Dict:






									'''simple docstring'''

									for tokenizer, tf_tokenizer in zip(self.tokenizers      ,							self.tf_tokenizers				):
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tokenizer(snake_case      ,							return_tensors='tf'      ,							padding='longest'				)
																							A         =					tf_tokenizer(snake_case				)

																							for key in python_outputs.keys():
																														self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape				)				)
																														self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]      ,							tf.intaa				) == tf_outputs[key]				)				)



		@slow
		def 					A_		( self  :    Tuple				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf_tokenizer(self.paired_sentences				)
																A         =					tf_tokenizer(
																    text=[sentence[0] for sentence in self.paired_sentences]      ,							text_pair=[sentence[1] for sentence in self.paired_sentences]      ,							)
																for key in merged_outputs.keys():
																							self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]      ,							tf.intaa				) == separated_outputs[key]				)				)



		@slow
		def 					A_		( self  :    Any				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf.function(snake_case				)
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tf.constant(snake_case				)
																							A         =					compiled_tokenizer(snake_case				)
																							A         =					tf_tokenizer(snake_case				)

																							for key in eager_outputs.keys():
																														self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]				)				)



		@slow
		def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					ModelToSave(tokenizer=snake_case				)
																A         =					tf.convert_to_tensor(self.test_sentences				)
																A         =					model(snake_case				)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																							A         =					Path(snake_case				) / 'saved.model'
																							model.save(snake_case				)
																							A         =					tf.keras.models.load_model(snake_case				)
																A         =					loaded_model(snake_case				)
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output				)				)      ,							1E-5				)



