
"""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

lowerCAmelCase__				=					logging.get_logger(__name__)





@add_end_docstrings(__lowercase )
class 			snake_case  (				__lowercase ):

							def __init__(self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )
									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(SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

							def __call__(self							,							SCREAMING_SNAKE_CASE_							,							*SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=64							,							SCREAMING_SNAKE_CASE_ = 0							,							SCREAMING_SNAKE_CASE_ = 5_12 / 15_00							,							SCREAMING_SNAKE_CASE_ = 32							,							SCREAMING_SNAKE_CASE_ = 1							,							):

									"""simple docstring"""







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

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

									SCREAMING_SNAKE_CASE_    			=			grid_points.shape[1]
									SCREAMING_SNAKE_CASE_    			=			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							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			grid_points[:, i : i + points_per_batch, :, :]
											SCREAMING_SNAKE_CASE_    			=			input_labels[:, i : i + points_per_batch]
											SCREAMING_SNAKE_CASE_    			=			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 _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=0.88							,							SCREAMING_SNAKE_CASE_=0.95							,							SCREAMING_SNAKE_CASE_=0							,							SCREAMING_SNAKE_CASE_=1							,							):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			self.model(**SCREAMING_SNAKE_CASE_     )

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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=0.7							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[]
									SCREAMING_SNAKE_CASE_    			=			[]
									SCREAMING_SNAKE_CASE_    			=			[]
									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'''     )     )

									SCREAMING_SNAKE_CASE_    			=			torch.cat(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			torch.cat(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.image_processor.post_process_for_mask_generation(
									    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

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

									SCREAMING_SNAKE_CASE_    			=			{}
									if output_rle_mask:
											SCREAMING_SNAKE_CASE_    			=			rle_mask

									if output_bboxes_mask:
											SCREAMING_SNAKE_CASE_    			=			bounding_boxes

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

"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]							,							dtype=tf.intaa							,							)  # J'aime le camembert !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,							dtype=tf.floataa							,							)
									# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
									# camembert.eval()
									# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

									self.assertTrue(np.allclose(output[:, :3, :3].numpy()							,							expected_slice.numpy()							,							atol=1e-4     )     )