




'''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,
						)


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


_lowerCamelCase							:						int							     =  Dict[str, Any]
_lowerCamelCase							:						List[str]							     =  List[Prediction]





@add_end_docstrings(UpperCAmelCase_							)
class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






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

							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,  **__UpperCamelCase			):
							_snake_case     	=	{}
							_snake_case     	=	{}
							if "subtask" in kwargs:
									_snake_case     	=	kwargs["subtask"]
									_snake_case     	=	kwargs["subtask"]
							if "threshold" in kwargs:
									_snake_case     	=	kwargs["threshold"]
							if "mask_threshold" in kwargs:
									_snake_case     	=	kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
									_snake_case     	=	kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs






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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase=None			):
							_snake_case     	=	load_image(__UpperCamelCase			)
							_snake_case     	=	[(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
									if subtask is None:
											_snake_case     	=	{}
									else:
											_snake_case     	=	{"task_inputs": [subtask]}
									_snake_case     	=	self.image_processor(images=[image],  return_tensors="pt",  **__UpperCamelCase			)
									_snake_case     	=	self.tokenizer(
									    inputs["task_inputs"],  padding="max_length",  max_length=self.model.config.task_seq_len,  return_tensors=self.framework,  )["input_ids"]
							else:
									_snake_case     	=	self.image_processor(images=[image],  return_tensors="pt"			)
							_snake_case     	=	target_size
							return inputs






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							_snake_case     	=	model_inputs.pop("target_size"			)
							_snake_case     	=	self.model(**__UpperCamelCase			)
							_snake_case     	=	target_size
							return model_outputs






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase=None,  __UpperCamelCase=0.9,  __UpperCamelCase=0.5,  __UpperCamelCase=0.5			):
							_snake_case     	=	None
							if subtask in {"panoptic", None} and hasattr(self.image_processor,  "post_process_panoptic_segmentation"			):
									_snake_case     	=	self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor,  "post_process_instance_segmentation"			):
									_snake_case     	=	self.image_processor.post_process_instance_segmentation

							if fn is not None:
									_snake_case     	=	fn(
									    __UpperCamelCase,  threshold=__UpperCamelCase,  mask_threshold=__UpperCamelCase,  overlap_mask_area_threshold=__UpperCamelCase,  target_sizes=model_outputs["target_size"],  )[0]

									_snake_case     	=	[]
									_snake_case     	=	outputs["segmentation"]

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

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

									_snake_case     	=	[]
									_snake_case     	=	outputs.numpy()
									_snake_case     	=	np.unique(__UpperCamelCase			)

									for label in labels:
											_snake_case     	=	(segmentation == label) * 255
											_snake_case     	=	Image.fromarray(mask.astype(np.uinta			),  mode="L"			)
											_snake_case     	=	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











'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_lowerCamelCase							:						int							     =  {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			"""flaubert"""
					lowercase__   =			{
					    """hidden_size""": """emb_dim""",
					    """num_attention_heads""": """n_heads""",
					    """num_hidden_layers""": """n_layers""",
					    """n_words""": """vocab_size""",  # For backward compatibility
					}






					def __init__(   self,  __UpperCamelCase=False,  __UpperCamelCase=0.0,  __UpperCamelCase=30_145,  __UpperCamelCase=2_048,  __UpperCamelCase=12,  __UpperCamelCase=16,  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=1,  __UpperCamelCase=True,  __UpperCamelCase=512,  __UpperCamelCase=2_048**-0.5,  __UpperCamelCase=1E-12,  __UpperCamelCase=0.02,  __UpperCamelCase=0,  __UpperCamelCase=1,  __UpperCamelCase=2,  __UpperCamelCase=3,  __UpperCamelCase=5,  __UpperCamelCase=True,  __UpperCamelCase="first",  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=0.1,  __UpperCamelCase=5,  __UpperCamelCase=5,  __UpperCamelCase=0,  __UpperCamelCase=0,  __UpperCamelCase=2,  __UpperCamelCase=0,  **__UpperCamelCase,  ):
							_snake_case     	=	pre_norm
							_snake_case     	=	layerdrop
							_snake_case     	=	vocab_size
							_snake_case     	=	emb_dim
							_snake_case     	=	n_layers
							_snake_case     	=	n_heads
							_snake_case     	=	dropout
							_snake_case     	=	attention_dropout
							_snake_case     	=	gelu_activation
							_snake_case     	=	sinusoidal_embeddings
							_snake_case     	=	causal
							_snake_case     	=	asm
							_snake_case     	=	n_langs
							_snake_case     	=	use_lang_emb
							_snake_case     	=	layer_norm_eps
							_snake_case     	=	bos_index
							_snake_case     	=	eos_index
							_snake_case     	=	pad_index
							_snake_case     	=	unk_index
							_snake_case     	=	mask_index
							_snake_case     	=	is_encoder
							_snake_case     	=	max_position_embeddings
							_snake_case     	=	embed_init_std
							_snake_case     	=	init_std
							_snake_case     	=	summary_type
							_snake_case     	=	summary_use_proj
							_snake_case     	=	summary_activation
							_snake_case     	=	summary_proj_to_labels
							_snake_case     	=	summary_first_dropout
							_snake_case     	=	start_n_top
							_snake_case     	=	end_n_top
							_snake_case     	=	mask_token_id
							_snake_case     	=	lang_id

							if "n_words" in kwargs:
									_snake_case     	=	kwargs["n_words"]

							super().__init__(pad_token_id=__UpperCamelCase,  bos_token_id=__UpperCamelCase,  **__UpperCamelCase			)





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							if self.task == "multiple-choice":
									_snake_case     	=	{0: "batch", 1: "choice", 2: "sequence"}
							else:
									_snake_case     	=	{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]			)





