





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

lowercase    : str    	=  logging.get_logger(__name__)


lowercase    : Any    	=  Dict[str, Any]
lowercase    : Any    	=  List[Prediction]

@add_end_docstrings(__UpperCAmelCase )
class  __A(						__UpperCAmelCase ):

     def __init__(      self,       *A,       **A      ):





            """simple docstring"""

            super().__init__(*A,       **A      )

            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 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            _UpperCamelCase              =						{}
            if "threshold" in kwargs:
                   _UpperCamelCase              =						kwargs['''threshold''']
            return {}, {}, postprocess_kwargs

     def __call__(      self,       *A,       **A      ):





            """simple docstring"""

            return super().__call__(*A,       **A      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						load_image(A      )
            _UpperCamelCase              =						torch.IntTensor([[image.height, image.width]]      )
            _UpperCamelCase              =						self.image_processor(images=[image],       return_tensors='''pt'''      )
            if self.tokenizer is not None:
                   _UpperCamelCase              =						self.tokenizer(text=inputs['''words'''],       boxes=inputs['''boxes'''],       return_tensors='''pt'''      )
            _UpperCamelCase              =						target_size
            return inputs

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						model_inputs.pop('''target_size'''      )
            _UpperCamelCase              =						self.model(**A      )
            _UpperCamelCase              =						outputs.__class__({'''target_size''': target_size, **outputs}      )
            if self.tokenizer is not None:
                   _UpperCamelCase              =						model_inputs['''bbox''']
            return model_outputs

     def 	_UpperCamelCase     (      self,       A,       A=0.9      ):





            """simple docstring"""

            _UpperCamelCase              =						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              =						target_size[0].tolist()

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

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

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

            return annotation



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

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













import os
import sys
import unittest


lowercase    : Any    	=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase    : int    	=  os.path.join(git_repo_path, """src""", """transformers""")

lowercase    : str    	=  """
{0} = None
"""

lowercase    : Union[str, Any]    	=  """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


lowercase    : Optional[Any]    	=  """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""

class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''      )
            self.assertIsNone(A      )

            _UpperCamelCase              =						find_backend('''    if not is_tokenizers_available():'''      )
            self.assertEqual(A,       '''tokenizers'''      )

            _UpperCamelCase              =						find_backend('''    if not is_tensorflow_text_available():'''      )
            self.assertEqual(A,       '''tensorflow_text'''      )

            _UpperCamelCase              =						find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''      )
            self.assertEqual(A,       '''sentencepiece_and_tokenizers'''      )

            _UpperCamelCase              =						find_backend(
                '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''      )
            self.assertEqual(A,       '''sentencepiece_and_tensorflow_text'''      )

            _UpperCamelCase              =						find_backend(
                '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''      )
            self.assertEqual(A,       '''sentencepiece_and_tokenizers_and_vision'''      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						read_init()
            # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
            self.assertIn('''torch''',       A      )
            self.assertIn('''tensorflow_text''',       A      )
            self.assertIn('''sentencepiece_and_tokenizers''',       A      )

            # Likewise, we can't assert on the exact content of a key
            self.assertIn('''BertModel''',       objects['''torch''']      )
            self.assertIn('''TFBertModel''',       objects['''tf''']      )
            self.assertIn('''FlaxBertModel''',       objects['''flax''']      )
            self.assertIn('''BertModel''',       objects['''torch''']      )
            self.assertIn('''TFBertTokenizer''',       objects['''tensorflow_text''']      )
            self.assertIn('''convert_slow_tokenizer''',       objects['''sentencepiece_and_tokenizers''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						create_dummy_object('''CONSTANT''',       '''\'torch\''''      )
            self.assertEqual(A,       '''\nCONSTANT = None\n'''      )

            _UpperCamelCase              =						create_dummy_object('''function''',       '''\'torch\''''      )
            self.assertEqual(
                A,       '''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''      )

            _UpperCamelCase              =						'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
            _UpperCamelCase              =						create_dummy_object('''FakeClass''',       '''\'torch\''''      )
            self.assertEqual(A,       A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
            _UpperCamelCase              =						create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}      )
            self.assertEqual(dummy_files['''torch'''],       A      )






