


'''simple docstring'''



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A	=get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class 		_snake_case					(     a__       ,   unittest.TestCase	):
					lowerCAmelCase      :str   =					XGLMTokenizer
					lowerCAmelCase      :List[str]   =					XGLMTokenizerFast
					lowerCAmelCase      :List[str]   =					True
					lowerCAmelCase      :Union[str, Any]   =					True




					def 				snake_case__						(      self):
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase__  :					int        =     XGLMTokenizer(__UpperCamelCase							,							keep_accents=__UpperCamelCase)
								tokenizer.save_pretrained(self.tmpdirname)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     """<pad>"""
								UpperCAmelCase__  :					Any        =     1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase)							,							__UpperCamelCase)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase)							,							__UpperCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     list(self.get_tokenizer().get_vocab().keys())

								self.assertEqual(vocab_keys[0]							,							"""<s>""")
								self.assertEqual(vocab_keys[1]							,							"""<pad>""")
								self.assertEqual(len(__UpperCamelCase)							,							1008)




					def 				snake_case__						(      self):
								self.assertEqual(self.get_tokenizer().vocab_size							,							1008)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     XGLMTokenizer(__UpperCamelCase							,							keep_accents=__UpperCamelCase)

								UpperCAmelCase__  :					Union[str, Any]        =     tokenizer.tokenize("""This is a test""")
								self.assertListEqual(__UpperCamelCase							,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(__UpperCamelCase)							,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							,							)

								UpperCAmelCase__  :					str        =     tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
								self.assertListEqual(
								    __UpperCamelCase							,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """9""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """é""",
								        """.""",
								    ]							,							)
								UpperCAmelCase__  :					Optional[Any]        =     tokenizer.convert_tokens_to_ids(__UpperCamelCase)
								self.assertListEqual(
								    __UpperCamelCase							,							[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ]							,							)

								UpperCAmelCase__  :					Tuple        =     tokenizer.convert_ids_to_tokens(__UpperCamelCase)
								self.assertListEqual(
								    __UpperCamelCase							,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """<unk>""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """<unk>""",
								        """.""",
								    ]							,							)




					@cached_property
					def 				snake_case__						(      self):
								return XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")




					def 				snake_case__						(      self):
								with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(__UpperCamelCase							,							f.name)
											UpperCAmelCase__  :					Union[str, Any]        =     XGLMTokenizer(f.name							,							keep_accents=__UpperCamelCase)
											UpperCAmelCase__  :					List[str]        =     pickle.dumps(__UpperCamelCase)
								pickle.loads(__UpperCamelCase)




					def 				snake_case__						(      self):
								if not self.test_rust_tokenizer:
											return

								UpperCAmelCase__  :					int        =     self.get_tokenizer()
								UpperCAmelCase__  :					Dict        =     self.get_rust_tokenizer()

								UpperCAmelCase__  :					str        =     """I was born in 92000, and this is falsé."""

								UpperCAmelCase__  :					Union[str, Any]        =     tokenizer.tokenize(__UpperCamelCase)
								UpperCAmelCase__  :					Dict        =     rust_tokenizer.tokenize(__UpperCamelCase)
								self.assertListEqual(__UpperCamelCase							,							__UpperCamelCase)

								UpperCAmelCase__  :					str        =     tokenizer.encode(__UpperCamelCase							,							add_special_tokens=__UpperCamelCase)
								UpperCAmelCase__  :					Any        =     rust_tokenizer.encode(__UpperCamelCase							,							add_special_tokens=__UpperCamelCase)
								self.assertListEqual(__UpperCamelCase							,							__UpperCamelCase)

								UpperCAmelCase__  :					str        =     self.get_rust_tokenizer()
								UpperCAmelCase__  :					Optional[int]        =     tokenizer.encode(__UpperCamelCase)
								UpperCAmelCase__  :					List[Any]        =     rust_tokenizer.encode(__UpperCamelCase)
								self.assertListEqual(__UpperCamelCase							,							__UpperCamelCase)




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     """Hello World!"""
								UpperCAmelCase__  :					Tuple        =     [2, 3_1227, 4447, 35]

								self.assertListEqual(__UpperCamelCase							,							self.big_tokenizer.encode(__UpperCamelCase))




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     (
								    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
								    """ add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
								)
								# fmt: off
								UpperCAmelCase__  :					Tuple        =     [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
								# fmt: on

								self.assertListEqual(__UpperCamelCase							,							self.big_tokenizer.encode(__UpperCamelCase))




					@slow
					def 				snake_case__						(      self):
								# fmt: off
								UpperCAmelCase__  :					Dict        =     {
								    """input_ids""": [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
								    """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
								}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=__UpperCamelCase							,							model_name="""facebook/xglm-564M"""							,							padding=__UpperCamelCase							,							)



'''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

__A	=logging.get_logger(__name__)


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


@add_end_docstrings(a__	)
class 		_snake_case					(     a__	):




					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):
								UpperCAmelCase__  :					int        =     {}
								if "threshold" in kwargs:
											UpperCAmelCase__  :					List[str]        =     kwargs["""threshold"""]
								return {}, {}, postprocess_kwargs




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




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[Any]        =     load_image(_lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     torch.IntTensor([[image.height, image.width]])
								UpperCAmelCase__  :					str        =     self.image_processor(images=[image]							,							return_tensors="""pt""")
								if self.tokenizer is not None:
											UpperCAmelCase__  :					Tuple        =     self.tokenizer(text=inputs["""words"""]							,							boxes=inputs["""boxes"""]							,							return_tensors="""pt""")
								UpperCAmelCase__  :					str        =     target_size
								return inputs




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[int]        =     model_inputs.pop("""target_size""")
								UpperCAmelCase__  :					Optional[Any]        =     self.model(**_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     outputs.__class__({"""target_size""": target_size, **outputs})
								if self.tokenizer is not None:
											UpperCAmelCase__  :					int        =     model_inputs["""bbox"""]
								return model_outputs




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=0.9):
								UpperCAmelCase__  :					Optional[Any]        =     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.
											UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     target_size[0].tolist()

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

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

											UpperCAmelCase__  :					Tuple        =     scores.tolist()
											UpperCAmelCase__  :					int        =     [self.model.config.idalabel[label.item()] for label in labels]
											UpperCAmelCase__  :					int        =     [self._get_bounding_box(_lowerCamelCase) for box in boxes]

											# {"scores": [...], ...} --> [{"score":x, ...}, ...]
											UpperCAmelCase__  :					Dict        =     ["""score""", """label""", """box"""]
											UpperCAmelCase__  :					Optional[int]        =     [
											    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.""")
								UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__  :					Union[str, Any]        =     box.int().tolist()
								UpperCAmelCase__  :					Dict        =     {
								    """xmin""": xmin,
								    """ymin""": ymin,
								    """xmax""": xmax,
								    """ymax""": ymax,
								}
								return bbox