
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,
						)


__a        = logging.get_logger(__name__)


__a        = Dict[str, Any]
__a        = List[Prediction]
@add_end_docstrings(UpperCAmelCase							)
class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	def __init__(	self  :      Dict	,							*SCREAMING_SNAKE_CASE_  :      List[Any]	,							**SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					str:
					super().__init__(*SCREAMING_SNAKE_CASE_	,							**SCREAMING_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 						_lowercase  (	self  :      List[str]	,							**SCREAMING_SNAKE_CASE_  :      Any	)	->					Optional[int]:
					lowercase_					  = {}
					lowercase_					  = {}
					if "subtask" in kwargs:
									lowercase_					  = kwargs['''subtask''']
									lowercase_					  = kwargs['''subtask''']
					if "threshold" in kwargs:
									lowercase_					  = kwargs['''threshold''']
					if "mask_threshold" in kwargs:
									lowercase_					  = kwargs['''mask_threshold''']
					if "overlap_mask_area_threshold" in kwargs:
									lowercase_					  = kwargs['''overlap_mask_area_threshold''']

					return preprocess_kwargs, {}, postprocess_kwargs
	def __call__(	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Dict	,							**SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					Union[Predictions, List[Prediction]]:
					return super().__call__(SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Any	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=None	)	->					Optional[Any]:
					lowercase_					  = load_image(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = [(image.height, image.width)]
					if self.model.config.__class__.__name__ == "OneFormerConfig":
									if subtask is None:
													lowercase_					  = {}
									else:
													lowercase_					  = {'''task_inputs''': [subtask]}
									lowercase_					  = self.image_processor(images=[image]	,							return_tensors='''pt'''	,							**SCREAMING_SNAKE_CASE_	)
									lowercase_					  = self.tokenizer(
									    inputs['''task_inputs''']	,							padding='''max_length'''	,							max_length=self.model.config.task_seq_len	,							return_tensors=self.framework	,							)['''input_ids''']
					else:
									lowercase_					  = self.image_processor(images=[image]	,							return_tensors='''pt'''	)
					lowercase_					  = target_size
					return inputs
	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					Any:
					lowercase_					  = model_inputs.pop('''target_size'''	)
					lowercase_					  = self.model(**SCREAMING_SNAKE_CASE_	)
					lowercase_					  = target_size
					return model_outputs




	def 						_lowercase  (	self  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Tuple=None	,							SCREAMING_SNAKE_CASE_  :      Dict=0.9	,							SCREAMING_SNAKE_CASE_  :      int=0.5	,							SCREAMING_SNAKE_CASE_  :      Any=0.5	)	->					str:
					lowercase_					  = None
					if subtask in {"panoptic", None} and hasattr(self.image_processor	,							'''post_process_panoptic_segmentation'''	):
									lowercase_					  = self.image_processor.post_process_panoptic_segmentation
					elif subtask in {"instance", None} and hasattr(self.image_processor	,							'''post_process_instance_segmentation'''	):
									lowercase_					  = self.image_processor.post_process_instance_segmentation

					if fn is not None:
									lowercase_					  = fn(
									    SCREAMING_SNAKE_CASE_	,							threshold=SCREAMING_SNAKE_CASE_	,							mask_threshold=SCREAMING_SNAKE_CASE_	,							overlap_mask_area_threshold=SCREAMING_SNAKE_CASE_	,							target_sizes=model_outputs['''target_size''']	,							)[0]

									lowercase_					  = []
									lowercase_					  = outputs['''segmentation''']

									for segment in outputs["segments_info"]:
													lowercase_					  = (segmentation == segment['''id''']) * 2_5_5
													lowercase_					  = Image.fromarray(mask.numpy().astype(np.uinta	)	,							mode='''L'''	)
													lowercase_					  = self.model.config.idalabel[segment['''label_id''']]
													lowercase_					  = segment['''score''']
													annotation.append({'''score''': score, '''label''': label, '''mask''': mask}	)

					elif subtask in {"semantic", None} and hasattr(self.image_processor	,							'''post_process_semantic_segmentation'''	):
									lowercase_					  = self.image_processor.post_process_semantic_segmentation(
									    SCREAMING_SNAKE_CASE_	,							target_sizes=model_outputs['''target_size''']	)[0]

									lowercase_					  = []
									lowercase_					  = outputs.numpy()
									lowercase_					  = np.unique(SCREAMING_SNAKE_CASE_	)

									for label in labels:
													lowercase_					  = (segmentation == label) * 2_5_5
													lowercase_					  = Image.fromarray(mask.astype(np.uinta	)	,							mode='''L'''	)
													lowercase_					  = 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






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  :      str	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_3	,							SCREAMING_SNAKE_CASE_  :      int=7	,							SCREAMING_SNAKE_CASE_  :      Tuple=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      Dict=True	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=9_9	,							SCREAMING_SNAKE_CASE_  :      Any=2_4	,							SCREAMING_SNAKE_CASE_  :      int=3_2	,							SCREAMING_SNAKE_CASE_  :      Any=5	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=3_7	,							SCREAMING_SNAKE_CASE_  :      Optional[int]="gelu"	,							SCREAMING_SNAKE_CASE_  :      Any=0.1	,							SCREAMING_SNAKE_CASE_  :      List[Any]=0.1	,							SCREAMING_SNAKE_CASE_  :      Dict=5_1_2	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_6	,							SCREAMING_SNAKE_CASE_  :      List[Any]=2	,							SCREAMING_SNAKE_CASE_  :      List[str]=0.02	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							)	->					Optional[int]:
					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = seq_length
					lowercase_					  = is_training
					lowercase_					  = use_attention_mask
					lowercase_					  = use_token_type_ids
					lowercase_					  = use_labels
					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = embedding_size
					lowercase_					  = num_hidden_layers
					lowercase_					  = num_attention_heads
					lowercase_					  = intermediate_size
					lowercase_					  = hidden_act
					lowercase_					  = hidden_dropout_prob
					lowercase_					  = attention_probs_dropout_prob
					lowercase_					  = max_position_embeddings
					lowercase_					  = type_vocab_size
					lowercase_					  = type_sequence_label_size
					lowercase_					  = initializer_range
					lowercase_					  = num_choices
	def 						_lowercase  (	self  :      Optional[int]	)	->					Tuple:
					lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size	)

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

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

					lowercase_					  = 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 						_lowercase  (	self  :      Any	)	->					str:
					lowercase_					  = self.prepare_config_and_inputs()
					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = config_and_inputs
					lowercase_					  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict

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



	"""simple docstring"""




	a      :List[str]   					=     True

	a      :List[Any]   					=     (
	    (
	        FlaxElectraModel,
	        FlaxElectraForCausalLM,
	        FlaxElectraForMaskedLM,
	        FlaxElectraForPreTraining,
	        FlaxElectraForTokenClassification,
	        FlaxElectraForQuestionAnswering,
	        FlaxElectraForMultipleChoice,
	        FlaxElectraForSequenceClassification,
	    )
	    if is_flax_available()
	    else ()
	)
	def 						_lowercase  (	self  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = FlaxElectraModelTester(self	)




	@slow
	def 						_lowercase  (	self  :      List[str]	)	->					int:
					for model_class_name in self.all_model_classes:
									if model_class_name == FlaxElectraForMaskedLM:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-generator'''	)
									else:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-discriminator'''	)
									lowercase_					  = model(np.ones((1, 1)	)	)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




