

'''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

UpperCamelCase_							=					logging.get_logger(__name__)


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





@add_end_docstrings(lowercase__			)
class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Any		,				*UpperCAmelCase__    :		Any		,				**UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										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_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()      )      )







				def    lowerCamelCase_  (					self    :		Dict		,				**UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										lowercase   :       Dict								={}
										if "threshold" in kwargs:
																lowercase   :       str								=kwargs['''threshold''']
										return {}, {}, postprocess_kwargs







				def __call__(					self    :		Tuple		,				*UpperCAmelCase__    :		int		,				**UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										return super().__call__(*UpperCAmelCase__		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=load_image(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=torch.IntTensor([[image.height, image.width]]      )
										lowercase   :       int								=self.image_processor(images=[image]		,				return_tensors='''pt'''      )
										if self.tokenizer is not None:
																lowercase   :       int								=self.tokenizer(text=inputs['''words''']		,				boxes=inputs['''boxes''']		,				return_tensors='''pt'''      )
										lowercase   :       str								=target_size
										return inputs







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Any								=model_inputs.pop('''target_size'''      )
										lowercase   :       Union[str, Any]								=self.model(**UpperCAmelCase__      )
										lowercase   :       Any								=outputs.__class__({'''target_size''': target_size, **outputs}      )
										if self.tokenizer is not None:
																lowercase   :       List[str]								=model_inputs['''bbox''']
										return model_outputs







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Dict=0.9      ):
										'''simple docstring'''
										lowercase   :       Dict								=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.
																lowercase		,  lowercase   :       Union[str, Any]								=target_size[0].tolist()

																def unnormalize(UpperCAmelCase__    :		Union[str, Any]      ):
																						return self._get_bounding_box(
																						    torch.Tensor(
																						        [
																						            (width * bbox[0] / 1000),
																						            (height * bbox[1] / 1000),
																						            (width * bbox[2] / 1000),
																						            (height * bbox[3] / 1000),
																						        ]      )      )

																lowercase		,  lowercase   :       List[Any]								=model_outputs['''logits'''].squeeze(0      ).softmax(dim=-1      ).max(dim=-1      )
																lowercase   :       Optional[int]								=[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
																lowercase   :       Dict								=[unnormalize(UpperCAmelCase__      ) for bbox in model_outputs['''bbox'''].squeeze(0      )]
																lowercase   :       List[str]								=['''score''', '''label''', '''box''']
																lowercase   :       Any								=[dict(zip(UpperCAmelCase__		,				UpperCAmelCase__      )      ) for vals in zip(scores.tolist()		,				UpperCAmelCase__		,				UpperCAmelCase__      ) if vals[0] > threshold]
										else:
																# This is a regular ForObjectDetectionModel
																lowercase   :       Tuple								=self.image_processor.post_process_object_detection(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      )
																lowercase   :       int								=raw_annotations[0]
																lowercase   :       Dict								=raw_annotation['''scores''']
																lowercase   :       Optional[Any]								=raw_annotation['''labels''']
																lowercase   :       List[str]								=raw_annotation['''boxes''']

																lowercase   :       List[Any]								=scores.tolist()
																lowercase   :       str								=[self.model.config.idalabel[label.item()] for label in labels]
																lowercase   :       List[str]								=[self._get_bounding_box(UpperCAmelCase__      ) for box in boxes]

																# {"scores": [...], ...} --> [{"score":x, ...}, ...]
																lowercase   :       str								=['''score''', '''label''', '''box''']
																lowercase   :       Optional[int]								=[
																    dict(zip(UpperCAmelCase__		,				UpperCAmelCase__      )      )
																    for vals in zip(raw_annotation['''scores''']		,				raw_annotation['''labels''']		,				raw_annotation['''boxes''']      )
																]

										return annotation







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		"torch.Tensor"      ):
										'''simple docstring'''
										if self.framework != "pt":
																raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''      )
										lowercase		,  lowercase		,  lowercase		,  lowercase   :       Union[str, Any]								=box.int().tolist()
										lowercase   :       Optional[Any]								={
										    '''xmin''': xmin,
										    '''ymin''': ymin,
										    '''xmax''': xmax,
										    '''ymax''': ymax,
										}
										return bbox








'''simple docstring'''





import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						None
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						'tokenizer_file'
				lowerCamelCase_							=						{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										super().setUp()
										lowercase   :       Union[str, Any]								=BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''      )
										tokenizer.save_pretrained(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return BloomTokenizerFast.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=self.get_rust_tokenizer()

										lowercase   :       List[str]								=['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
										lowercase   :       Any								=[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

										lowercase   :       Any								=tokenizer.batch_encode_plus(UpperCAmelCase__      )['''input_ids''']
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       int								=tokenizer.batch_decode(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		Any=6      ):
										'''simple docstring'''
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																						lowercase   :       Optional[int]								=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
																						# tokenizer_r.pad_token = None # Hotfixing padding = None
																						# Simple input
																						lowercase   :       Tuple								='''This is a simple input'''
																						lowercase   :       int								=['''This is a simple input 1''', '''This is a simple input 2''']
																						lowercase   :       Optional[Any]								=('''This is a simple input''', '''This is a pair''')
																						lowercase   :       int								=[
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						try:
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )

																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																						except ValueError:
																												self.fail('''Bloom Tokenizer should be able to deal with padding'''      )

																						lowercase   :       Optional[int]								=None  # Hotfixing padding = None
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.get_rust_tokenizer()
										lowercase   :       Dict								=load_dataset('''xnli'''		,				'''all_languages'''		,				split='''test'''		,				streaming=UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=next(iter(UpperCAmelCase__      )      )['''premise''']  # pick up one data
										lowercase   :       int								=list(sample_data.values()      )

										lowercase   :       Any								=list(map(tokenizer.encode		,				UpperCAmelCase__      )      )
										lowercase   :       List[str]								=[tokenizer.decode(UpperCAmelCase__		,				clean_up_tokenization_spaces=UpperCAmelCase__      ) for x in output_tokens]
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
										# any sequence length constraints. This test of the parent class will fail since it relies on the
										# maximum sequence length of the positoonal embeddings.
										self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map      )		,				1      )
										self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()      )[0]      )		,				1      )





