


'''simple docstring'''


import unittest

import numpy as np

from transformers import ElectraConfig, 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():
				from transformers.models.electra.modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				)





class    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							def __init__(   self   , __snake_case   , __snake_case=13   , __snake_case=7   , __snake_case=True   , __snake_case=True   , __snake_case=True   , __snake_case=True   , __snake_case=99   , __snake_case=24   , __snake_case=32   , __snake_case=5   , __snake_case=4   , __snake_case=37   , __snake_case="gelu"   , __snake_case=0.1   , __snake_case=0.1   , __snake_case=512   , __snake_case=16   , __snake_case=2   , __snake_case=0.02   , __snake_case=4   , ):
											_SCREAMING_SNAKE_CASE			:	str							=				parent
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				batch_size
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				seq_length
											_SCREAMING_SNAKE_CASE			:	int							=				is_training
											_SCREAMING_SNAKE_CASE			:	List[str]							=				use_attention_mask
											_SCREAMING_SNAKE_CASE			:	str							=				use_token_type_ids
											_SCREAMING_SNAKE_CASE			:	int							=				use_labels
											_SCREAMING_SNAKE_CASE			:	str							=				vocab_size
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				hidden_size
											_SCREAMING_SNAKE_CASE			:	List[str]							=				embedding_size
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				num_hidden_layers
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				intermediate_size
											_SCREAMING_SNAKE_CASE			:	Tuple							=				hidden_act
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				hidden_dropout_prob
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE			:	List[str]							=				max_position_embeddings
											_SCREAMING_SNAKE_CASE			:	Tuple							=				type_vocab_size
											_SCREAMING_SNAKE_CASE			:	List[str]							=				type_sequence_label_size
											_SCREAMING_SNAKE_CASE			:	List[str]							=				initializer_range
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_choices






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size  )

											_SCREAMING_SNAKE_CASE			:	Dict							=				None
											if self.use_attention_mask:
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				random_attention_mask([self.batch_size, self.seq_length]  )

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				None
											if self.use_token_type_ids:
															_SCREAMING_SNAKE_CASE			:	Any							=				ids_tensor([self.batch_size, self.seq_length]   , self.type_vocab_size  )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				ElectraConfig(
											    vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , embedding_size=self.embedding_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   , initializer_range=self.initializer_range   , )

											return config, input_ids, token_type_ids, attention_mask






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Any							=				config_and_inputs
											_SCREAMING_SNAKE_CASE			:	Tuple							=				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
											return config, inputs_dict





@require_flax
class    lowercase__  (						_snake_case					,					unittest.TestCase ):





							'''simple docstring'''


							A_							:				Optional[Any]				     =  True

							A_							:				List[Any]				     =  (
							    (
							        FlaxElectraModel,
							        FlaxElectraForCausalLM,
							        FlaxElectraForMaskedLM,
							        FlaxElectraForPreTraining,
							        FlaxElectraForTokenClassification,
							        FlaxElectraForQuestionAnswering,
							        FlaxElectraForMultipleChoice,
							        FlaxElectraForSequenceClassification,
							    )
							    if is_flax_available()
							    else ()
							)






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				FlaxElectraModelTester(self  )






							@slow
							def UpperCAmelCase_     (   self  ):
											for model_class_name in self.all_model_classes:
															if model_class_name == FlaxElectraForMaskedLM:
																			_SCREAMING_SNAKE_CASE			:	int							=				model_class_name.from_pretrained("""google/electra-small-generator"""  )
															else:
																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				model_class_name.from_pretrained("""google/electra-small-discriminator"""  )
															_SCREAMING_SNAKE_CASE			:	List[str]							=				model(np.ones((1, 1)  )  )
															self.assertIsNotNone(__snake_case  )








'''simple docstring'''


from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				from ..models.auto.modeling_auto import (
				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
				)


UpperCAmelCase_		:       Optional[int]							       =		logging.get_logger(__name__)


UpperCAmelCase_		:       List[str]							       =		Dict[str, Any]
UpperCAmelCase_		:       List[str]							       =		List[Prediction]





@add_end_docstrings(_snake_case )
class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							def __init__(   self   , *__snake_case   , **__snake_case  ):
											super().__init__(*__snake_case   , **__snake_case  )

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

											requires_backends(self   , """vision"""  )
											self.check_model_type(
											    dict(
											        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
											        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
											        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
											        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()  )  )






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				{}
											_SCREAMING_SNAKE_CASE			:	Any							=				{}
											if "subtask" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Any							=				kwargs["""subtask"""]
															_SCREAMING_SNAKE_CASE			:	List[str]							=				kwargs["""subtask"""]
											if "threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				kwargs["""threshold"""]
											if "mask_threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Dict							=				kwargs["""mask_threshold"""]
											if "overlap_mask_area_threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Tuple							=				kwargs["""overlap_mask_area_threshold"""]

											return preprocess_kwargs, {}, postprocess_kwargs






							def __call__(   self   , __snake_case   , **__snake_case  ):
											return super().__call__(__snake_case   , **__snake_case  )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=None  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				load_image(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				[(image.height, image.width)]
											if self.model.config.__class__.__name__ == "OneFormerConfig":
															if subtask is None:
																			_SCREAMING_SNAKE_CASE			:	int							=				{}
															else:
																			_SCREAMING_SNAKE_CASE			:	str							=				{"""task_inputs""": [subtask]}
															_SCREAMING_SNAKE_CASE			:	Any							=				self.image_processor(images=[image]   , return_tensors="""pt"""   , **__snake_case  )
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.tokenizer(
															    inputs["""task_inputs"""]   , padding="""max_length"""   , max_length=self.model.config.task_seq_len   , return_tensors=self.framework   , )["""input_ids"""]
											else:
															_SCREAMING_SNAKE_CASE			:	int							=				self.image_processor(images=[image]   , return_tensors="""pt"""  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				target_size
											return inputs






							def UpperCAmelCase_     (   self   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				model_inputs.pop("""target_size"""  )
											_SCREAMING_SNAKE_CASE			:	int							=				self.model(**__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				target_size
											return model_outputs






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=None   , __snake_case=0.9   , __snake_case=0.5   , __snake_case=0.5  ):
											_SCREAMING_SNAKE_CASE			:	str							=				None
											if subtask in {"panoptic", None} and hasattr(self.image_processor   , """post_process_panoptic_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	int							=				self.image_processor.post_process_panoptic_segmentation
											elif subtask in {"instance", None} and hasattr(self.image_processor   , """post_process_instance_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	str							=				self.image_processor.post_process_instance_segmentation

											if fn is not None:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				fn(
															    __snake_case   , threshold=__snake_case   , mask_threshold=__snake_case   , overlap_mask_area_threshold=__snake_case   , target_sizes=model_outputs["""target_size"""]   , )[0]

															_SCREAMING_SNAKE_CASE			:	Dict							=				[]
															_SCREAMING_SNAKE_CASE			:	int							=				outputs["""segmentation"""]

															for segment in outputs["segments_info"]:
																			_SCREAMING_SNAKE_CASE			:	str							=				(segmentation == segment["""id"""]) * 255
																			_SCREAMING_SNAKE_CASE			:	int							=				Image.fromarray(mask.numpy().astype(np.uinta  )   , mode="""L"""  )
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.model.config.idalabel[segment["""label_id"""]]
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				segment["""score"""]
																			annotation.append({"""score""": score, """label""": label, """mask""": mask}  )

											elif subtask in {"semantic", None} and hasattr(self.image_processor   , """post_process_semantic_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.image_processor.post_process_semantic_segmentation(
															    __snake_case   , target_sizes=model_outputs["""target_size"""]  )[0]

															_SCREAMING_SNAKE_CASE			:	str							=				[]
															_SCREAMING_SNAKE_CASE			:	List[str]							=				outputs.numpy()
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				np.unique(__snake_case  )

															for label in labels:
																			_SCREAMING_SNAKE_CASE			:	Any							=				(segmentation == label) * 255
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				Image.fromarray(mask.astype(np.uinta  )   , mode="""L"""  )
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				self.model.config.idalabel[label]
																			annotation.append({"""score""": None, """label""": label, """mask""": mask}  )
											else:
															raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model  )}"""  )
											return annotation




