




'''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

lowerCamelCase_			=						logging.get_logger(__name__)


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







@add_end_docstrings(A       )
class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Union[str, Any]  ,				*__lowerCamelCase :     Any  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      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 							lowerCAmelCase_      (   self :     Dict  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{}
      if "threshold" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["threshold"]
      return {}, {}, postprocess_kwargs



   def __call__(   self :     Dict  ,				*__lowerCamelCase :     List[Any]  ,				**__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return super().__call__(*__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     int   ):

      """simple docstring"""
      _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 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      _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 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]=0.9   ):

      """simple docstring"""
      _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 :     Any   ):
            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 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     "torch.Tensor"   ):

      """simple docstring"""
      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









'''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 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return None







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      return None







class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						[
       # (model_name, model_kwargs)
       ('''bert-base-cased''', {}),
       ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
   ]



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      from transformers import BertModel

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

      with TemporaryDirectory() as bert_save_dir:
         _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig(vocab_size=len(__lowerCamelCase   )   )   )
         model.save_pretrained(__lowerCamelCase   )
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				__lowerCamelCase   )



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(Path(__lowerCamelCase   )   )

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



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(__lowerCamelCase   )

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



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[int]=None  ,				**__lowerCamelCase :     int   ):

      """simple docstring"""
      try:
         # Compute path
         with TemporaryDirectory() as tempdir:
            _SCREAMING_SNAKE_CASE	=				Path(__lowerCamelCase   ).joinpath("model.onnx"   )

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

         # Export
         convert(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   )

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



   @require_torch
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"pt"   )



   @require_tf
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      from transformers import TFBertModel

      _SCREAMING_SNAKE_CASE	=				TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"tf"   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				FeatureExtractionPipeline(__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				infer_shapes(__lowerCamelCase  ,				__lowerCamelCase   )

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

      # 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 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				["input_ids", "attention_mask", "token_type_ids"]
      _SCREAMING_SNAKE_CASE	=				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

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

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

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

      # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncNonContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

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

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



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				generate_identified_filename(Path("/home/something/my_fake_model.onnx"   )  ,				"-test"   )
      self.assertEqual("/home/something/my_fake_model-test.onnx"  ,				generated.as_posix()   )

