


'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow


class 		_snake_case					:




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								return None


class 		_snake_case					:




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								return None


class 		_snake_case					(     unittest.TestCase	):
					lowerCAmelCase      :int   =					[
					    # (model_name, model_kwargs)
					    ('''bert-base-cased''', {}),
					    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
					]




					@require_tf
					@slow
					def 				snake_case__						(      self):
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(a_							,							"""tf"""							,							12							,							**a_)




					@require_torch
					@slow
					def 				snake_case__						(      self):
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(a_							,							"""pt"""							,							12							,							**a_)




					@require_torch
					@slow
					def 				snake_case__						(      self):
								from transformers import BertModel

								UpperCAmelCase__  :					Dict        =     ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
								with NamedTemporaryFile(mode="""w+t""") as vocab_file:
											vocab_file.write("""\n""".join(a_))
											vocab_file.flush()
											UpperCAmelCase__  :					Dict        =     BertTokenizerFast(vocab_file.name)

								with TemporaryDirectory() as bert_save_dir:
											UpperCAmelCase__  :					Tuple        =     BertModel(BertConfig(vocab_size=len(a_)))
											model.save_pretrained(a_)
											self._test_export(a_							,							"""pt"""							,							12							,							a_)




					@require_tf
					@slow
					def 				snake_case__						(      self):
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											UpperCAmelCase__  :					Dict        =     self._test_export(a_							,							"""tf"""							,							12							,							**a_)
											UpperCAmelCase__  :					List[Any]        =     quantize(Path(a_))

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(a_).stat().st_size:
														self.fail("""Quantized model is bigger than initial ONNX model""")




					@require_torch
					@slow
					def 				snake_case__						(      self):
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											UpperCAmelCase__  :					int        =     self._test_export(a_							,							"""pt"""							,							12							,							**a_)
											UpperCAmelCase__  :					Union[str, Any]        =     quantize(a_)

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(a_).stat().st_size:
														self.fail("""Quantized model is bigger than initial ONNX model""")




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase=None							,							**_lowerCamelCase):
								try:
											# Compute path
											with TemporaryDirectory() as tempdir:
														UpperCAmelCase__  :					Tuple        =     Path(a_).joinpath("""model.onnx""")

											# Remove folder if exists
											if path.parent.exists():
														path.parent.rmdir()

											# Export
											convert(a_							,							a_							,							a_							,							a_							,							a_							,							**a_)

											return path
								except Exception as e:
											self.fail(a_)




					@require_torch
					@require_tokenizers
					@slow
					def 				snake_case__						(      self):
								from transformers import BertModel

								UpperCAmelCase__  :					str        =     BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
								UpperCAmelCase__  :					int        =     BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
								self._test_infer_dynamic_axis(a_							,							a_							,							"""pt""")




					@require_tf
					@require_tokenizers
					@slow
					def 				snake_case__						(      self):
								from transformers import TFBertModel

								UpperCAmelCase__  :					List[str]        =     TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
								UpperCAmelCase__  :					List[str]        =     BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
								self._test_infer_dynamic_axis(a_							,							a_							,							"""tf""")




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     FeatureExtractionPipeline(a_							,							a_)

								UpperCAmelCase__  :					Dict        =     ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
								UpperCAmelCase__  :					Tuple        =     infer_shapes(a_							,							a_)

								# Assert all variables are present
								self.assertEqual(len(a_)							,							len(a_))
								self.assertTrue(all(var_name in shapes for var_name in variable_names))
								self.assertSequenceEqual(variable_names[:3]							,							a_)
								self.assertSequenceEqual(variable_names[3:]							,							a_)

								# Assert inputs are {0: batch, 1: sequence}
								for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
											self.assertDictEqual(shapes[var_name]							,							{0: """batch""", 1: """sequence"""})

								# Assert outputs are {0: batch, 1: sequence} and {0: batch}
								self.assertDictEqual(shapes["""output_0"""]							,							{0: """batch""", 1: """sequence"""})
								self.assertDictEqual(shapes["""output_1"""]							,							{0: """batch"""})




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     ["input_ids", "attention_mask", "token_type_ids"]
								UpperCAmelCase__  :					Optional[int]        =     {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
								UpperCAmelCase__  :					Tuple        =     ensure_valid_input(FuncContiguousArgs()							,							a_							,							a_)

								# Should have exactly the same number of args (all are valid)
								self.assertEqual(len(a_)							,							3)

								# Should have exactly the same input names
								self.assertEqual(set(a_)							,							set(a_))

								# Parameter should be reordered according to their respective place in the function:
								# (input_ids, token_type_ids, attention_mask)
								self.assertEqual(a_							,							(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]))

								# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
								UpperCAmelCase__  :					Any        =     ensure_valid_input(FuncNonContiguousArgs()							,							a_							,							a_)

								# Should have exactly the one arg (all before the one not provided "some_other_args")
								self.assertEqual(len(a_)							,							1)
								self.assertEqual(len(a_)							,							1)

								# Should have only "input_ids"
								self.assertEqual(inputs_args[0]							,							tokens["""input_ids"""])
								self.assertEqual(ordered_input_names[0]							,							"""input_ids""")




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     generate_identified_filename(Path("""/home/something/my_fake_model.onnx""")							,							"""-test""")
								self.assertEqual("""/home/something/my_fake_model-test.onnx"""							,							generated.as_posix())



'''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