
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			:       List[str]      =						logging.get_logger(__name__)


_snake_case			:       Optional[Any]      =						Dict[str, Any]
_snake_case			:       Optional[int]      =						List[Prediction]





@add_end_docstrings(__a		)
class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						def __init__( self       :Union[str, Any]		,						*lowerCAmelCase__       :List[Any]		,						**lowerCAmelCase__       :Dict    )     ->List[str]:
										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 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						**lowerCAmelCase__       :int    )     ->Any:
										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       :Optional[Any]		,						lowerCAmelCase__       :Optional[Any]		,						**lowerCAmelCase__       :Any    )     ->Union[Predictions, List[Prediction]]:
										return super().__call__(lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Dict=None    )     ->Optional[Any]:
										lowercase						    =			load_image(lowerCAmelCase__    )
										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"		,						**lowerCAmelCase__    )
														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 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :Any    )     ->List[str]:
										lowercase						    =			model_inputs.pop("target_size"    )
										lowercase						    =			self.model(**lowerCAmelCase__    )
										lowercase						    =			target_size
										return model_outputs





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :Any=None		,						lowerCAmelCase__       :Optional[int]=0.9		,						lowerCAmelCase__       :str=0.5		,						lowerCAmelCase__       :int=0.5    )     ->Tuple:
										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(
														    lowerCAmelCase__		,						threshold=lowerCAmelCase__		,						mask_threshold=lowerCAmelCase__		,						overlap_mask_area_threshold=lowerCAmelCase__		,						target_sizes=model_outputs["target_size"]		,						)[0]

														lowercase						    =			[]
														lowercase						    =			outputs["segmentation"]

														for segment in outputs["segments_info"]:
																		lowercase						    =			(segmentation == segment["id"]) * 255
																		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(
														    lowerCAmelCase__		,						target_sizes=model_outputs["target_size"]    )[0]

														lowercase						    =			[]
														lowercase						    =			outputs.numpy()
														lowercase						    =			np.unique(lowerCAmelCase__    )

														for label in labels:
																		lowercase						    =			(segmentation == label) * 255
																		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


from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_snake_case			:       Dict      =						{
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Any							      =				'''reformer'''
						UpperCamelCase			:     List[str]							      =				['''past_buckets_states''']
						UpperCamelCase			:     Optional[int]							      =				{}





						def __init__( self       :Dict		,						lowerCAmelCase__       :Any=64		,						lowerCAmelCase__       :str=["local", "lsh", "local", "lsh", "local", "lsh"]		,						lowerCAmelCase__       :Optional[Any]=1.0		,						lowerCAmelCase__       :List[str]=True		,						lowerCAmelCase__       :Optional[Any]=[64, 64]		,						lowerCAmelCase__       :Tuple=[64, 192]		,						lowerCAmelCase__       :Union[str, Any]=0		,						lowerCAmelCase__       :Optional[Any]=2		,						lowerCAmelCase__       :List[str]=512		,						lowerCAmelCase__       :Any=None		,						lowerCAmelCase__       :int="relu"		,						lowerCAmelCase__       :Tuple=0.05		,						lowerCAmelCase__       :int=256		,						lowerCAmelCase__       :Optional[Any]=0.02		,						lowerCAmelCase__       :Optional[int]=False		,						lowerCAmelCase__       :Optional[Any]=1E-1_2		,						lowerCAmelCase__       :int=1		,						lowerCAmelCase__       :Optional[Any]=0		,						lowerCAmelCase__       :Any=0.05		,						lowerCAmelCase__       :Optional[int]=64		,						lowerCAmelCase__       :List[Any]=64		,						lowerCAmelCase__       :Tuple=0.0		,						lowerCAmelCase__       :Tuple=1		,						lowerCAmelCase__       :List[str]=0		,						lowerCAmelCase__       :Dict=4096		,						lowerCAmelCase__       :Tuple=12		,						lowerCAmelCase__       :Tuple=None		,						lowerCAmelCase__       :Union[str, Any]=1		,						lowerCAmelCase__       :int=0		,						lowerCAmelCase__       :Optional[Any]=320		,						lowerCAmelCase__       :Optional[Any]=False		,						lowerCAmelCase__       :Optional[int]=True		,						lowerCAmelCase__       :str=None		,						**lowerCAmelCase__       :Any		,						)     ->Any:
										lowercase						    =			hash_seed
										lowercase						    =			vocab_size
										lowercase						    =			attention_head_size
										lowercase						    =			hidden_size
										lowercase						    =			num_attention_heads
										lowercase						    =			num_hashes
										lowercase						    =			len(lowerCAmelCase__    )
										lowercase						    =			tuple(lowerCAmelCase__    ) if isinstance(lowerCAmelCase__		,						lowerCAmelCase__    ) else num_buckets
										lowercase						    =			lsh_attn_chunk_length
										lowercase						    =			local_attn_chunk_length
										lowercase						    =			lsh_num_chunks_after
										lowercase						    =			lsh_num_chunks_before
										lowercase						    =			local_num_chunks_after
										lowercase						    =			local_num_chunks_before
										lowercase						    =			hidden_act
										lowercase						    =			feed_forward_size
										lowercase						    =			hidden_dropout_prob
										lowercase						    =			lsh_attention_probs_dropout_prob
										lowercase						    =			local_attention_probs_dropout_prob
										lowercase						    =			max_position_embeddings
										lowercase						    =			initializer_range
										lowercase						    =			layer_norm_eps
										lowercase						    =			axial_pos_embds
										lowercase						    =			tuple(lowerCAmelCase__    )
										lowercase						    =			tuple(lowerCAmelCase__    )
										lowercase						    =			axial_norm_std
										lowercase						    =			chunk_size_lm_head
										lowercase						    =			attn_layers
										lowercase						    =			use_cache
										lowercase						    =			classifier_dropout
										super().__init__(
										    pad_token_id=lowerCAmelCase__		,						eos_token_id=lowerCAmelCase__		,						is_decoder=lowerCAmelCase__		,						tie_word_embeddings=lowerCAmelCase__		,						**lowerCAmelCase__		,						)
