
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,
						)


snake_case__  							=							logging.get_logger(__name__)


snake_case__  							=							Dict[str, Any]
snake_case__  							=							List[Prediction]







@add_end_docstrings(__lowerCamelCase					)
class 							UpperCAmelCase	(   __lowerCamelCase					):

					def __init__(			self      :					Tuple ,      *lowerCAmelCase      :					List[Any] ,      **lowerCAmelCase      :					Any					):
											super().__init__(*lowerCAmelCase ,      **lowerCAmelCase					)

											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        _lowerCAmelCase	(			self      :					Union[str, Any] ,      **lowerCAmelCase      :					Optional[int]					):
											lowercase       :    Union[str, Any]											=  {}
											lowercase       :    List[Any]											=  {}
											if "subtask" in kwargs:
																	lowercase       :    str											=  kwargs['''subtask''']
																	lowercase       :    List[Any]											=  kwargs['''subtask''']
											if "threshold" in kwargs:
																	lowercase       :    Tuple											=  kwargs['''threshold''']
											if "mask_threshold" in kwargs:
																	lowercase       :    Any											=  kwargs['''mask_threshold''']
											if "overlap_mask_area_threshold" in kwargs:
																	lowercase       :    List[str]											=  kwargs['''overlap_mask_area_threshold''']

											return preprocess_kwargs, {}, postprocess_kwargs

					def __call__(			self      :					int ,      lowerCAmelCase      :					Any ,      **lowerCAmelCase      :					Optional[Any]					):
											return super().__call__(lowerCAmelCase ,      **lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					Union[str, Any] ,      lowerCAmelCase      :					List[Any] ,      lowerCAmelCase      :					Any=None					):
											lowercase       :    str											=  load_image(lowerCAmelCase					)
											lowercase       :    List[Any]											=  [(image.height, image.width)]
											if self.model.config.__class__.__name__ == "OneFormerConfig":
																	if subtask is None:
																							lowercase       :    Optional[Any]											=  {}
																	else:
																							lowercase       :    List[str]											=  {'''task_inputs''': [subtask]}
																	lowercase       :    Tuple											=  self.image_processor(images=[image] ,      return_tensors='''pt''' ,      **lowerCAmelCase					)
																	lowercase       :    Any											=  self.tokenizer(
																	    inputs['''task_inputs'''] ,      padding='''max_length''' ,      max_length=self.model.config.task_seq_len ,      return_tensors=self.framework ,      )['''input_ids''']
											else:
																	lowercase       :    Tuple											=  self.image_processor(images=[image] ,      return_tensors='''pt'''					)
											lowercase       :    Union[str, Any]											=  target_size
											return inputs

					def        _lowerCAmelCase	(			self      :					Dict ,      lowerCAmelCase      :					Any					):
											lowercase       :    Union[str, Any]											=  model_inputs.pop('''target_size'''					)
											lowercase       :    Optional[int]											=  self.model(**lowerCAmelCase					)
											lowercase       :    int											=  target_size
											return model_outputs




					def        _lowerCAmelCase	(			self      :					List[Any] ,      lowerCAmelCase      :					Any ,      lowerCAmelCase      :					Union[str, Any]=None ,      lowerCAmelCase      :					str=0.9 ,      lowerCAmelCase      :					Dict=0.5 ,      lowerCAmelCase      :					int=0.5					):
											lowercase       :    int											=  None
											if subtask in {"panoptic", None} and hasattr(self.image_processor ,      '''post_process_panoptic_segmentation'''					):
																	lowercase       :    str											=  self.image_processor.post_process_panoptic_segmentation
											elif subtask in {"instance", None} and hasattr(self.image_processor ,      '''post_process_instance_segmentation'''					):
																	lowercase       :    Union[str, Any]											=  self.image_processor.post_process_instance_segmentation

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

																	lowercase       :    Optional[int]											=  []
																	lowercase       :    Any											=  outputs['''segmentation''']

																	for segment in outputs["segments_info"]:
																							lowercase       :    Tuple											=  (segmentation == segment['''id''']) * 255
																							lowercase       :    int											=  Image.fromarray(mask.numpy().astype(np.uinta					) ,      mode='''L'''					)
																							lowercase       :    Any											=  self.model.config.idalabel[segment['''label_id''']]
																							lowercase       :    int											=  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       :    List[str]											=  self.image_processor.post_process_semantic_segmentation(
																	    lowerCAmelCase ,      target_sizes=model_outputs['''target_size''']					)[0]

																	lowercase       :    int											=  []
																	lowercase       :    Dict											=  outputs.numpy()
																	lowercase       :    List[Any]											=  np.unique(lowerCAmelCase					)

																	for label in labels:
																							lowercase       :    Union[str, Any]											=  (segmentation == label) * 255
																							lowercase       :    int											=  Image.fromarray(mask.astype(np.uinta					) ,      mode='''L'''					)
																							lowercase       :    Dict											=  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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				snake_case__  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


