



"""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

__SCREAMING_SNAKE_CASE  :						List[Any]    							=      logging.get_logger(__name__)


@add_end_docstrings(__a    )
class 							lowerCamelCase_(   __a    ):





					'''simple docstring'''



					def __init__(    self				,       **lowerCamelCase__   ):
									super().__init__(**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(snake_case__   )

					def 			snake_case__      (    self				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				{}
									_lowerCamelCase        =				{}
									_lowerCamelCase        =				{}
									# preprocess args
									if "points_per_batch" in kwargs:
													_lowerCamelCase        =				kwargs['''points_per_batch''']
									if "points_per_crop" in kwargs:
													_lowerCamelCase        =				kwargs['''points_per_crop''']
									if "crops_n_layers" in kwargs:
													_lowerCamelCase        =				kwargs['''crops_n_layers''']
									if "crop_overlap_ratio" in kwargs:
													_lowerCamelCase        =				kwargs['''crop_overlap_ratio''']
									if "crop_n_points_downscale_factor" in kwargs:
													_lowerCamelCase        =				kwargs['''crop_n_points_downscale_factor''']
									# postprocess args
									if "pred_iou_thresh" in kwargs:
													_lowerCamelCase        =				kwargs['''pred_iou_thresh''']
									if "stability_score_offset" in kwargs:
													_lowerCamelCase        =				kwargs['''stability_score_offset''']
									if "mask_threshold" in kwargs:
													_lowerCamelCase        =				kwargs['''mask_threshold''']
									if "stability_score_thresh" in kwargs:
													_lowerCamelCase        =				kwargs['''stability_score_thresh''']
									if "crops_nms_thresh" in kwargs:
													_lowerCamelCase        =				kwargs['''crops_nms_thresh''']
									if "output_rle_mask" in kwargs:
													_lowerCamelCase        =				kwargs['''output_rle_mask''']
									if "output_bboxes_mask" in kwargs:
													_lowerCamelCase        =				kwargs['''output_bboxes_mask''']
									return preprocess_kwargs, forward_params, postprocess_kwargs

					def __call__(    self				,       lowerCamelCase__				,       *lowerCamelCase__				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									return super().__call__(snake_case__				,       *snake_case__				,       num_workers=snake_case__				,       batch_size=snake_case__				,       **snake_case__   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=6_4				,       lowerCamelCase__ = 0				,       lowerCamelCase__ = 5_1_2 / 1_5_0_0				,       lowerCamelCase__ = 3_2				,       lowerCamelCase__ = 1				,       ):
									_lowerCamelCase        =				load_image(snake_case__   )
									_lowerCamelCase        =				self.image_processor.size['''longest_edge''']
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				self.image_processor.generate_crop_boxes(
									    snake_case__				,       snake_case__				,       snake_case__				,       snake_case__				,       snake_case__				,       snake_case__   )
									_lowerCamelCase        =				self.image_processor(images=snake_case__				,       return_tensors='''pt'''   )

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

									_lowerCamelCase        =				grid_points.shape[1]
									_lowerCamelCase        =				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				,       snake_case__				,       snake_case__   ):
													_lowerCamelCase        =				grid_points[:, i : i + points_per_batch, :, :]
													_lowerCamelCase        =				input_labels[:, i : i + points_per_batch]
													_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0.8_8				,       lowerCamelCase__=0.9_5				,       lowerCamelCase__=0				,       lowerCamelCase__=1				,       ):
									_lowerCamelCase        =				model_inputs.pop('''input_boxes'''   )
									_lowerCamelCase        =				model_inputs.pop('''is_last'''   )
									_lowerCamelCase        =				model_inputs.pop('''original_sizes'''   ).tolist()
									_lowerCamelCase        =				model_inputs.pop('''reshaped_input_sizes'''   ).tolist()

									_lowerCamelCase        =				self.model(**snake_case__   )

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





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=0.7				,       ):
									_lowerCamelCase        =				[]
									_lowerCamelCase        =				[]
									_lowerCamelCase        =				[]
									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'''   )   )

									_lowerCamelCase        =				torch.cat(snake_case__   )
									_lowerCamelCase        =				torch.cat(snake_case__   )
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				self.image_processor.post_process_for_mask_generation(
									    snake_case__				,       snake_case__				,       snake_case__				,       snake_case__   )

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

									_lowerCamelCase        =				{}
									if output_rle_mask:
													_lowerCamelCase        =				rle_mask

									if output_bboxes_mask:
													_lowerCamelCase        =				bounding_boxes

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











"""simple docstring"""
import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax

			from transformers.models.big_bird.modeling_flax_big_bird import (
			    FlaxBigBirdForCausalLM,
			    FlaxBigBirdForMaskedLM,
			    FlaxBigBirdForMultipleChoice,
			    FlaxBigBirdForPreTraining,
			    FlaxBigBirdForQuestionAnswering,
			    FlaxBigBirdForSequenceClassification,
			    FlaxBigBirdForTokenClassification,
			    FlaxBigBirdModel,
			)


class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=2				,       lowerCamelCase__=5_6				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=9_9				,       lowerCamelCase__=3_2				,       lowerCamelCase__=2				,       lowerCamelCase__=2				,       lowerCamelCase__=7				,       lowerCamelCase__="gelu_new"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=5_1_2				,       lowerCamelCase__=1_6				,       lowerCamelCase__=2				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=4				,       lowerCamelCase__="block_sparse"				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=2				,       lowerCamelCase__=3				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				seq_length
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_attention_mask
									_lowerCamelCase        =				use_token_type_ids
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				max_position_embeddings
									_lowerCamelCase        =				type_vocab_size
									_lowerCamelCase        =				type_sequence_label_size
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				num_choices

									_lowerCamelCase        =				rescale_embeddings
									_lowerCamelCase        =				attention_type
									_lowerCamelCase        =				use_bias
									_lowerCamelCase        =				block_size
									_lowerCamelCase        =				num_random_blocks

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				None
									if self.use_attention_mask:
													_lowerCamelCase        =				random_attention_mask([self.batch_size, self.seq_length]   )

									_lowerCamelCase        =				None
									if self.use_token_type_ids:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size   )

									_lowerCamelCase        =				BigBirdConfig(
									    vocab_size=self.vocab_size				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       type_vocab_size=self.type_vocab_size				,       is_decoder=lowerCamelCase__				,       initializer_range=self.initializer_range				,       attention_type=self.attention_type				,       block_size=self.block_size				,       num_random_blocks=self.num_random_blocks				,       use_bias=self.use_bias				,       rescale_embeddings=self.rescale_embeddings				,       )

									return config, input_ids, token_type_ids, attention_mask





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				config_and_inputs
									_lowerCamelCase        =				{
									    '''input_ids''': input_ids,
									    '''token_type_ids''': token_type_ids,
									    '''attention_mask''': attention_mask,
									}
									return config, inputs_dict


@require_flax
class 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       List[str]										= (
					    (
					        FlaxBigBirdForCausalLM,
					        FlaxBigBirdModel,
					        FlaxBigBirdForPreTraining,
					        FlaxBigBirdForMaskedLM,
					        FlaxBigBirdForMultipleChoice,
					        FlaxBigBirdForQuestionAnswering,
					        FlaxBigBirdForSequenceClassification,
					        FlaxBigBirdForTokenClassification,
					    )
					    if is_flax_available()
					    else ()
					)

					lowercase__   :       Any										= False
					lowercase__   :       Optional[int]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				FlaxBigBirdModelTester(self   )

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_from_pretrained_save_pretrained()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_from_pretrained_with_no_automatic_init()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_no_automatic_init()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_hidden_states_output()

					@slow
					def 			snake_case__      (    self   ):
									for model_class_name in self.all_model_classes:
													_lowerCamelCase        =				model_class_name.from_pretrained('''google/bigbird-roberta-base'''   )
													self.assertIsNotNone(lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									if self.test_attn_probs:
													super().test_attention_outputs()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																	_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )
																	_lowerCamelCase        =				model_class(lowerCamelCase__   )

																	@jax.jit
																	def model_jitted(lowerCamelCase__				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
																					return model(input_ids=lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       **lowerCamelCase__   )

																	with self.subTest('''JIT Enabled'''   ):
																					_lowerCamelCase        =				model_jitted(**lowerCamelCase__   ).to_tuple()

																	with self.subTest('''JIT Disabled'''   ):
																					with jax.disable_jit():
																									_lowerCamelCase        =				model_jitted(**lowerCamelCase__   ).to_tuple()

																	self.assertEqual(len(lowerCamelCase__   )				,       len(lowerCamelCase__   )   )
																	for jitted_output, output in zip(lowerCamelCase__				,       lowerCamelCase__   ):
																					self.assertEqual(jitted_output.shape				,       output.shape   )





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=1e-5				,       lowerCamelCase__="outputs"				,       lowerCamelCase__=None   ):
									# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
									# an effort was done to return `attention_probs` (yet to be verified).
									if name.startswith('''outputs.attentions'''   ):
													return
									else:
													super().check_pt_flax_outputs(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
