
"""simple docstring"""





from typing import Any, Dict, List, Union

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 ..image_utils import load_image


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__magic_name__       =			logging.get_logger(__name__)


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




		"""simple docstring"""





		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				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_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))




		def 	snake_case_     (     self							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = {}
				if "threshold" in kwargs:
						__SCREAMING_SNAKE_CASE            = kwargs["""threshold"""]
				return {}, {}, postprocess_kwargs




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




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = load_image(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = torch.IntTensor([[image.height, image.width]])
				__SCREAMING_SNAKE_CASE            = self.image_processor(images=[image]							,  return_tensors="""pt""")
				if self.tokenizer is not None:
						__SCREAMING_SNAKE_CASE            = self.tokenizer(text=inputs["""words"""]							,  boxes=inputs["""boxes"""]							,  return_tensors="""pt""")
				__SCREAMING_SNAKE_CASE            = target_size
				return inputs




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = model_inputs.pop("""target_size""")
				__SCREAMING_SNAKE_CASE            = self.model(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = outputs.__class__({"""target_size""": target_size, **outputs})
				if self.tokenizer is not None:
						__SCREAMING_SNAKE_CASE            = model_inputs["""bbox"""]
				return model_outputs




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=0.9):
				__SCREAMING_SNAKE_CASE            = model_outputs["""target_size"""]
				if self.tokenizer is not None:
						# This is a LayoutLMForTokenClassification variant.
						# The OCR got the boxes and the model classified the words.
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = target_size[0].tolist()

						def unnormalize(lowerCAmelCase__):
								return self._get_bounding_box(
								    torch.Tensor(
								        [
								            (width * bbox[0] / 1_0_0_0),
								            (height * bbox[1] / 1_0_0_0),
								            (width * bbox[2] / 1_0_0_0),
								            (height * bbox[3] / 1_0_0_0),
								        ]))

						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = model_outputs["""logits"""].squeeze(0).softmax(dim=-1).max(dim=-1)
						__SCREAMING_SNAKE_CASE            = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
						__SCREAMING_SNAKE_CASE            = [unnormalize(lowerCAmelCase__) for bbox in model_outputs["""bbox"""].squeeze(0)]
						__SCREAMING_SNAKE_CASE            = ["""score""", """label""", """box"""]
						__SCREAMING_SNAKE_CASE            = [dict(zip(lowerCAmelCase__							,  lowerCAmelCase__)) for vals in zip(scores.tolist()							,  lowerCAmelCase__							,  lowerCAmelCase__) if vals[0] > threshold]
				else:
						# This is a regular ForObjectDetectionModel
						__SCREAMING_SNAKE_CASE            = self.image_processor.post_process_object_detection(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE            = raw_annotations[0]
						__SCREAMING_SNAKE_CASE            = raw_annotation["""scores"""]
						__SCREAMING_SNAKE_CASE            = raw_annotation["""labels"""]
						__SCREAMING_SNAKE_CASE            = raw_annotation["""boxes"""]

						__SCREAMING_SNAKE_CASE            = scores.tolist()
						__SCREAMING_SNAKE_CASE            = [self.model.config.idalabel[label.item()] for label in labels]
						__SCREAMING_SNAKE_CASE            = [self._get_bounding_box(lowerCAmelCase__) for box in boxes]

						# {"scores": [...], ...} --> [{"score":x, ...}, ...]
						__SCREAMING_SNAKE_CASE            = ["""score""", """label""", """box"""]
						__SCREAMING_SNAKE_CASE            = [
						    dict(zip(lowerCAmelCase__							,  lowerCAmelCase__))
						    for vals in zip(raw_annotation["""scores"""]							,  raw_annotation["""labels"""]							,  raw_annotation["""boxes"""])
						]

				return annotation




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				if self.framework != "pt":
						raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""")
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = box.int().tolist()
				__SCREAMING_SNAKE_CASE            = {
				    """xmin""": xmin,
				    """ymin""": ymin,
				    """xmax""": xmax,
				    """ymax""": ymax,
				}
				return bbox








import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


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

__lowerCAmelCase	: Optional[Any]  				={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase	: List[str]  				={
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase	: Optional[int]  				={
    'gpt-neox-20b': 2_0_4_8,
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			Dict						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Any						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		['''input_ids''', '''attention_mask''']


				def __init__(       self			:int			,			lowerCAmelCase__			:Any=None			,			lowerCAmelCase__			:Optional[Any]=None			,			lowerCAmelCase__			:List[Any]=None			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:Dict="<|endoftext|>"			,			lowerCAmelCase__			:Union[str, Any]=False			,			**lowerCAmelCase__			:List[str]			,			)		->			Any:
										super().__init__(
										    lowerCAmelCase__			,			lowerCAmelCase__			,			tokenizer_file=lowerCAmelCase__			,			unk_token=lowerCAmelCase__			,			bos_token=lowerCAmelCase__			,			eos_token=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__			,			**lowerCAmelCase__			,			)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
										if pre_tok_state.get('''add_prefix_space'''			,			lowerCAmelCase__	) != add_prefix_space:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		getattr(lowerCAmelCase__			,			pre_tok_state.pop('''type'''	)	)
																__SCREAMING_SNAKE_CASE						:     str					=		add_prefix_space
																__SCREAMING_SNAKE_CASE						:     Any					=		pre_tok_class(**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		add_prefix_space


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple[str]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self._tokenizer.model.save(lowerCAmelCase__			,			name=lowerCAmelCase__	)
										return tuple(lowerCAmelCase__	)



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:"Conversation"	)		->			List[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase__			,			add_special_tokens=lowerCAmelCase__	) + [self.eos_token_id]	)

										if len(lowerCAmelCase__	) > self.model_max_length:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		input_ids[-self.model_max_length :]
										return input_ids
