"""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__  :  int											=     logging.get_logger(__name__)


lowerCAmelCase__  :  List[Any]											=     Dict[str, Any]
lowerCAmelCase__  :  Dict											=     List[Prediction]
@add_end_docstrings(__UpperCAmelCase    )
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					def __init__(    self       :     Tuple   ,*lowerCamelCase__       :     List[Any]   ,**lowerCamelCase__       :     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 		__lowerCAmelCase						(    self       :     List[str]   ,**lowerCamelCase__       :     Optional[int]      ):
									UpperCAmelCase__					=							{}
									UpperCAmelCase__					=							{}
									if "subtask" in kwargs:
													UpperCAmelCase__					=							kwargs['subtask']
													UpperCAmelCase__					=							kwargs['subtask']
									if "threshold" in kwargs:
													UpperCAmelCase__					=							kwargs['threshold']
									if "mask_threshold" in kwargs:
													UpperCAmelCase__					=							kwargs['mask_threshold']
									if "overlap_mask_area_threshold" in kwargs:
													UpperCAmelCase__					=							kwargs['overlap_mask_area_threshold']

									return preprocess_kwargs, {}, postprocess_kwargs







					def __call__(    self       :     List[Any]   ,lowerCamelCase__       :     Union[str, Any]   ,**lowerCamelCase__       :     str      ):
									return super().__call__(lowerCamelCase__   ,**lowerCamelCase__      )







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







					def 		__lowerCAmelCase						(    self       :     str   ,lowerCamelCase__       :     Any      ):
									UpperCAmelCase__					=							model_inputs.pop('target_size'      )
									UpperCAmelCase__					=							self.model(**lowerCamelCase__      )
									UpperCAmelCase__					=							target_size
									return model_outputs







					def 		__lowerCAmelCase						(    self       :     Tuple   ,lowerCamelCase__       :     int   ,lowerCamelCase__       :     Union[str, Any]=None   ,lowerCamelCase__       :     List[str]=0.9   ,lowerCamelCase__       :     Optional[Any]=0.5   ,lowerCamelCase__       :     Optional[int]=0.5      ):
									UpperCAmelCase__					=							None
									if subtask in {"panoptic", None} and hasattr(self.image_processor   ,'post_process_panoptic_segmentation'      ):
													UpperCAmelCase__					=							self.image_processor.post_process_panoptic_segmentation
									elif subtask in {"instance", None} and hasattr(self.image_processor   ,'post_process_instance_segmentation'      ):
													UpperCAmelCase__					=							self.image_processor.post_process_instance_segmentation

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

													UpperCAmelCase__					=							[]
													UpperCAmelCase__					=							outputs['segmentation']

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

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

													UpperCAmelCase__					=							[]
													UpperCAmelCase__					=							outputs.numpy()
													UpperCAmelCase__					=							np.unique(lowerCamelCase__      )

													for label in labels:
																	UpperCAmelCase__					=							(segmentation == label) * 255
																	UpperCAmelCase__					=							Image.fromarray(mask.astype(np.uinta      )   ,mode='L'      )
																	UpperCAmelCase__					=							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 Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase__  :  Any											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"gpt2"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__(    self       :     Any   ,lowerCamelCase__       :     List[Any]=50_257   ,lowerCamelCase__       :     Union[str, Any]=1_024   ,lowerCamelCase__       :     Dict=768   ,lowerCamelCase__       :     List[str]=12   ,lowerCamelCase__       :     Dict=12   ,lowerCamelCase__       :     List[str]=None   ,lowerCamelCase__       :     List[str]="gelu_new"   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Dict=0.1   ,lowerCamelCase__       :     str=0.1   ,lowerCamelCase__       :     Union[str, Any]=1e-5   ,lowerCamelCase__       :     Optional[Any]=0.0_2   ,lowerCamelCase__       :     str="cls_index"   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     List[Any]=None   ,lowerCamelCase__       :     List[str]=True   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Tuple=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]=50_256   ,lowerCamelCase__       :     Union[str, Any]=50_256   ,lowerCamelCase__       :     Union[str, Any]=False   ,lowerCamelCase__       :     List[Any]=False   ,**lowerCamelCase__       :     Tuple   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							n_positions
									UpperCAmelCase__					=							n_embd
									UpperCAmelCase__					=							n_layer
									UpperCAmelCase__					=							n_head
									UpperCAmelCase__					=							n_inner
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							resid_pdrop
									UpperCAmelCase__					=							embd_pdrop
									UpperCAmelCase__					=							attn_pdrop
									UpperCAmelCase__					=							layer_norm_epsilon
									UpperCAmelCase__					=							initializer_range
									UpperCAmelCase__					=							summary_type
									UpperCAmelCase__					=							summary_use_proj
									UpperCAmelCase__					=							summary_activation
									UpperCAmelCase__					=							summary_first_dropout
									UpperCAmelCase__					=							summary_proj_to_labels
									UpperCAmelCase__					=							scale_attn_weights
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							scale_attn_by_inverse_layer_idx
									UpperCAmelCase__					=							reorder_and_upcast_attn

									UpperCAmelCase__					=							bos_token_id
									UpperCAmelCase__					=							eos_token_id

									super().__init__(bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,**lowerCamelCase__      )






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					def __init__(    self       :     List[str]   ,lowerCamelCase__       :     PretrainedConfig   ,lowerCamelCase__       :     str = "default"   ,lowerCamelCase__       :     List[PatchingSpec] = None   ,lowerCamelCase__       :     bool = False   ,):
									super().__init__(lowerCamelCase__   ,task=lowerCamelCase__   ,patching_specs=lowerCamelCase__   ,use_past=lowerCamelCase__      )
									if not getattr(self._config   ,'pad_token_id'   ,lowerCamelCase__      ):
													# TODO: how to do that better?
													UpperCAmelCase__					=							0







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
									if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__   ,direction='inputs'      )
													UpperCAmelCase__					=							{0: 'batch', 1: 'past_sequence + sequence'}
									else:
													UpperCAmelCase__					=							{0: 'batch', 1: 'sequence'}

									return common_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									return self._config.n_layer







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return self._config.n_head







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     PreTrainedTokenizer   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     Optional[TensorType] = None   ,):
									UpperCAmelCase__					=							super(lowerCamelCase__   ,self      ).generate_dummy_inputs(
									    lowerCamelCase__   ,batch_size=lowerCamelCase__   ,seq_length=lowerCamelCase__   ,is_pair=lowerCamelCase__   ,framework=lowerCamelCase__      )

									# We need to order the input in the way they appears in the forward()
									UpperCAmelCase__					=							OrderedDict({'input_ids': common_inputs['input_ids']}      )

									# Need to add the past_keys
									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
													else:
																	import torch

																	UpperCAmelCase__				,     UpperCAmelCase__					=							common_inputs['input_ids'].shape
																	# Not using the same length for past_key_values
																	UpperCAmelCase__					=							seqlen + 2
																	UpperCAmelCase__					=							(
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	UpperCAmelCase__					=							[
																	    (torch.zeros(lowerCamelCase__      ), torch.zeros(lowerCamelCase__      )) for _ in range(self.num_layers      )
																	]

									UpperCAmelCase__					=							common_inputs['attention_mask']
									if self.use_past:
													UpperCAmelCase__					=							ordered_inputs['attention_mask'].dtype
													UpperCAmelCase__					=							torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__   ,lowerCamelCase__   ,dtype=lowerCamelCase__      )]   ,dim=1      )

									return ordered_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 13



