import unittest

import numpy as np
import timeout_decorator  # noqa

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

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		__lowerCamelCase					:	Optional[int]       =  '''platform'''

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)




def       _snake_case	(   lowerCAmelCase							:   Any   , lowerCAmelCase							:   str   , lowerCAmelCase							:   int=None   , lowerCAmelCase							:   str=None   , lowerCAmelCase							:   Tuple=None   , lowerCAmelCase							:   Optional[Any]=None   , lowerCAmelCase							:   Any=None   , lowerCAmelCase							:   Union[str, Any]=None   , ):
		"""simple docstring"""







		if attention_mask is None:
				SCREAMING_SNAKE_CASE_  :		int				      =    np.where(input_ids != config.pad_token_id   , 1   , 0	)
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.where(decoder_input_ids != config.pad_token_id   , 1   , 0	)
		if head_mask is None:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.ones((config.encoder_layers, config.encoder_attention_heads)	)
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.ones((config.decoder_layers, config.decoder_attention_heads)	)
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.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": attention_mask,
		}






class 		a__						:

					def __init__(    self      :       Optional[int],_A      :       str,_A      :       int=13,_A      :       Union[str, Any]=7,_A      :       List[Any]=True,_A      :       Optional[int]=False,_A      :       Tuple=99,_A      :       Optional[int]=16,_A      :       List[str]=2,_A      :       int=4,_A      :       Any=4,_A      :       Any="gelu",_A      :       Union[str, Any]=0.1,_A      :       List[Any]=0.1,_A      :       Union[str, Any]=32,_A      :       List[str]=2,_A      :       Tuple=1,_A      :       Optional[int]=0,_A      :       List[Any]=0.02,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    parent
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    seq_length
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    is_training
							SCREAMING_SNAKE_CASE_  :		int				      =    use_labels
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    hidden_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		int				      =    num_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    intermediate_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    eos_token_id
							SCREAMING_SNAKE_CASE_  :		int				      =    pad_token_id
							SCREAMING_SNAKE_CASE_  :		Any				      =    bos_token_id
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    initializer_range

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1],self.vocab_size    ),3,self.vocab_size    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),dtype=np.intaa    )),-1    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    shift_tokens_right(_A,1,2    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    BlenderbotSmallConfig(
							    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_id=self.eos_token_id,bos_token_id=self.bos_token_id,pad_token_id=self.pad_token_id,initializer_range=self.initializer_range,use_cache=_A,)
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    prepare_blenderbot_inputs_dict(_A,_A,_A    )
							return config, inputs_dict

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.prepare_config_and_inputs()
							return config, inputs_dict

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       Tuple,_A      :       Tuple,_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    20
							SCREAMING_SNAKE_CASE_  :		Any				      =    model_class_name(_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model.encode(inputs_dict["input_ids"]    )

							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							SCREAMING_SNAKE_CASE_  :		int				      =    model.init_cache(decoder_input_ids.shape[0],_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length),dtype="i4"    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model.decode(
							    decoder_input_ids[:, :-1],_A,decoder_attention_mask=_A,past_key_values=_A,decoder_position_ids=_A,)

							SCREAMING_SNAKE_CASE_  :		Dict				      =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4"    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model.decode(
							    decoder_input_ids[:, -1:],_A,decoder_attention_mask=_A,past_key_values=outputs_cache.past_key_values,decoder_position_ids=_A,)

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model.decode(_A,_A    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
							self.parent.assertTrue(diff < 1E-3,msg=F'Max diff is {diff}'    )






					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       int,_A      :       Tuple,_A      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    20
							SCREAMING_SNAKE_CASE_  :		int				      =    model_class_name(_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model.encode(inputs_dict["input_ids"]    )

							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							SCREAMING_SNAKE_CASE_  :		Dict				      =    jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])    ),
							    ],axis=-1,)

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model.init_cache(decoder_input_ids.shape[0],_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)

							SCREAMING_SNAKE_CASE_  :		Dict				      =    model.decode(
							    decoder_input_ids[:, :-1],_A,decoder_attention_mask=_A,past_key_values=_A,decoder_position_ids=_A,)
							SCREAMING_SNAKE_CASE_  :		str				      =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4"    )
							SCREAMING_SNAKE_CASE_  :		int				      =    model.decode(
							    decoder_input_ids[:, -1:],_A,past_key_values=outputs_cache.past_key_values,decoder_attention_mask=_A,decoder_position_ids=_A,)

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    model.decode(_A,_A,decoder_attention_mask=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
							self.parent.assertTrue(diff < 1E-3,msg=F'Max diff is {diff}'    )







@require_flax
class 		a__						(					unittest.TestCase					):
					A	       =							99

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ],dtype=np.intaa,)

							SCREAMING_SNAKE_CASE_  :		Any				      =    input_ids.shape[0]
							SCREAMING_SNAKE_CASE_  :		Dict				      =    BlenderbotSmallConfig(
							    vocab_size=self.vocab_size,d_model=24,encoder_layers=2,decoder_layers=2,encoder_attention_heads=2,decoder_attention_heads=2,encoder_ffn_dim=32,decoder_ffn_dim=32,max_position_embeddings=48,eos_token_id=2,pad_token_id=1,bos_token_id=0,)
							return config, input_ids, batch_size

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    self._get_config_and_data()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    FlaxBlenderbotSmallForConditionalGeneration(_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    lm_model(input_ids=_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape,_A    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    BlenderbotSmallConfig(
							    vocab_size=self.vocab_size,d_model=14,encoder_layers=2,decoder_layers=2,encoder_attention_heads=2,decoder_attention_heads=2,encoder_ffn_dim=8,decoder_ffn_dim=8,max_position_embeddings=48,)
							SCREAMING_SNAKE_CASE_  :		Dict				      =    FlaxBlenderbotSmallForConditionalGeneration(_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],dtype=np.intaa    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],dtype=np.intaa    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    lm_model(input_ids=_A,decoder_input_ids=_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    (*summary.shape, config.vocab_size)
							self.assertEqual(outputs["logits"].shape,_A    )






					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],dtype=np.intaa    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    shift_tokens_right(_A,1,2    )
							SCREAMING_SNAKE_CASE_  :		int				      =    np.equal(_A,1    ).astype(np.floataa    ).sum()
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.equal(_A,1    ).astype(np.floataa    ).sum()
							self.assertEqual(shifted.shape,input_ids.shape    )
							self.assertEqual(_A,n_pad_before - 1    )
							self.assertTrue(np.equal(shifted[:, 0],2    ).all()    )







@require_flax
class 		a__						(					A__    ,		unittest.TestCase    ,		A__					):
					A	       =							True
					A	       =							(
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					A	       =							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    FlaxBlenderbotSmallModelTester(self    )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(_A,_A,_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(_A,_A,_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__    ):
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._prepare_for_class(_A,_A    )
											SCREAMING_SNAKE_CASE_  :		List[str]				      =    model_class(_A    )

											@jax.jit
											def encode_jitted(_A      :       Dict,_A      :       Optional[int]=None,**_A      :       int    ):
													return model.encode(input_ids=_A,attention_mask=_A    )

											with self.subTest("JIT Enabled"    ):
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    encode_jitted(**_A    ).to_tuple()

											with self.subTest("JIT Disabled"    ):
													with jax.disable_jit():
															SCREAMING_SNAKE_CASE_  :		Dict				      =    encode_jitted(**_A    ).to_tuple()

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

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__    ):
											SCREAMING_SNAKE_CASE_  :		str				      =    model_class(_A    )
											SCREAMING_SNAKE_CASE_  :		int				      =    model.encode(inputs_dict["input_ids"],inputs_dict["attention_mask"]    )

											SCREAMING_SNAKE_CASE_  :		Dict				      =    {
											    "decoder_input_ids": inputs_dict["decoder_input_ids"],
											    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
											    "encoder_outputs": encoder_outputs,
											}

											@jax.jit
											def decode_jitted(_A      :       str,_A      :       Any,_A      :       int    ):
													return model.decode(
													    decoder_input_ids=_A,decoder_attention_mask=_A,encoder_outputs=_A,)

											with self.subTest("JIT Enabled"    ):
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    decode_jitted(**_A    ).to_tuple()

											with self.subTest("JIT Disabled"    ):
													with jax.disable_jit():
															SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    decode_jitted(**_A    ).to_tuple()

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






					@slow
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    model_class_name.from_pretrained("facebook/blenderbot_small-90M"    )
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									SCREAMING_SNAKE_CASE_  :		str				      =    np.ones((1, 1)    ) * model.config.eos_token_id
									SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A    )
									self.assertIsNotNone(_A    )





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

_lowercase: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""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      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""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		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""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}
