

import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class  _A		:

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=13 ,					_SCREAMING_SNAKE_CASE=7 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=99 ,					_SCREAMING_SNAKE_CASE=32 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=4 ,					_SCREAMING_SNAKE_CASE=37 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=512 ,					_SCREAMING_SNAKE_CASE=16 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=4 ,					_SCREAMING_SNAKE_CASE=None ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     parent
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     batch_size
        SCREAMING_SNAKE_CASE_				:  Any           =     seq_length
        SCREAMING_SNAKE_CASE_				:  Dict           =     is_training
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     use_input_mask
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     use_token_type_ids
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     use_labels
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     vocab_size
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     hidden_size
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     num_hidden_layers
        SCREAMING_SNAKE_CASE_				:  List[str]           =     num_attention_heads
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     intermediate_size
        SCREAMING_SNAKE_CASE_				:  Dict           =     hidden_act
        SCREAMING_SNAKE_CASE_				:  List[str]           =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_				:  Dict           =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_				:  List[str]           =     max_position_embeddings
        SCREAMING_SNAKE_CASE_				:  str           =     type_vocab_size
        SCREAMING_SNAKE_CASE_				:  Tuple           =     type_sequence_label_size
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     initializer_range
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     num_labels
        SCREAMING_SNAKE_CASE_				:  Any           =     num_choices
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scope

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     None
        if self.use_input_mask:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     random_attention_mask([self.batch_size, self.seq_length]					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     None
        if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size					)

        SCREAMING_SNAKE_CASE_				:  Any           =     None
        SCREAMING_SNAKE_CASE_				:  List[str]           =     None
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_				:  int           =     ids_tensor([self.batch_size] ,					self.type_sequence_label_size					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels					)
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     ids_tensor([self.batch_size] ,					self.num_choices					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return OpenLlamaConfig(
            vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					is_decoder=_SCREAMING_SNAKE_CASE ,					initializer_range=self.initializer_range ,					use_stable_embedding=_SCREAMING_SNAKE_CASE ,					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     OpenLlamaModel(config=_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  Dict           =     model(_SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     model(_SCREAMING_SNAKE_CASE					)
        self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     True
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     OpenLlamaModel(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     model(
            _SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE ,					encoder_hidden_states=_SCREAMING_SNAKE_CASE ,					encoder_attention_mask=_SCREAMING_SNAKE_CASE ,					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     model(
            _SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE ,					encoder_hidden_states=_SCREAMING_SNAKE_CASE ,					)
        SCREAMING_SNAKE_CASE_				:  Any           =     model(_SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE					)
        self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     OpenLlamaForCausalLM(config=_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     model(_SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE ,					labels=_SCREAMING_SNAKE_CASE					)
        self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     True
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     True
        SCREAMING_SNAKE_CASE_				:  int           =     OpenLlamaForCausalLM(config=_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()

        # first forward pass
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(
            _SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE ,					encoder_hidden_states=_SCREAMING_SNAKE_CASE ,					encoder_attention_mask=_SCREAMING_SNAKE_CASE ,					use_cache=_SCREAMING_SNAKE_CASE ,					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        SCREAMING_SNAKE_CASE_				:  int           =     ids_tensor((self.batch_size, 3) ,					config.vocab_size					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     ids_tensor((self.batch_size, 3) ,					vocab_size=2					)

        # append to next input_ids and
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.cat([input_ids, next_tokens] ,					dim=-1					)
        SCREAMING_SNAKE_CASE_				:  Any           =     torch.cat([input_mask, next_mask] ,					dim=-1					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     model(
            _SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE ,					encoder_hidden_states=_SCREAMING_SNAKE_CASE ,					encoder_attention_mask=_SCREAMING_SNAKE_CASE ,					output_hidden_states=_SCREAMING_SNAKE_CASE ,					)['hidden_states'][0]
        SCREAMING_SNAKE_CASE_				:  Tuple           =     model(
            _SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE ,					encoder_hidden_states=_SCREAMING_SNAKE_CASE ,					encoder_attention_mask=_SCREAMING_SNAKE_CASE ,					past_key_values=_SCREAMING_SNAKE_CASE ,					output_hidden_states=_SCREAMING_SNAKE_CASE ,					)['hidden_states'][0]

        # select random slice
        SCREAMING_SNAKE_CASE_				:  int           =     ids_tensor((1,) ,					output_from_past.shape[-1]					).item()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     output_from_no_past[:, -3:, random_slice_idx].detach()
        SCREAMING_SNAKE_CASE_				:  Any           =     output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					atol=1e-3					)					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.prepare_config_and_inputs()
        (
            (
            SCREAMING_SNAKE_CASE_
        )       ,						(
            SCREAMING_SNAKE_CASE_
        )       ,						(
            SCREAMING_SNAKE_CASE_
        )       ,						(
            SCREAMING_SNAKE_CASE_
        )       ,						(
            SCREAMING_SNAKE_CASE_
        )       ,						(
            SCREAMING_SNAKE_CASE_
        )       ,						(
            SCREAMING_SNAKE_CASE_
        )       ,						
        )				:  Optional[Any]           =     config_and_inputs
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict

@require_torch
class  _A		(  __magic_name__   ,     __magic_name__   ,     __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  (
      (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
  )
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  (OpenLlamaForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE    :      List[str]       =  (
      {
          '''feature-extraction''': OpenLlamaModel,
          '''text-classification''': OpenLlamaForSequenceClassification,
          '''text-generation''': OpenLlamaForCausalLM,
          '''zero-shot''': OpenLlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE    :      List[str]       =  False
  SCREAMING_SNAKE_CASE    :      Tuple       =  False

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     OpenLlamaModelTester(self					)
        SCREAMING_SNAKE_CASE_				:  int           =     ConfigTester(self ,					config_class=_SCREAMING_SNAKE_CASE ,					hidden_size=37					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.config_tester.run_common_tests()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              SCREAMING_SNAKE_CASE_				:  Dict           =     type
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Tuple           =     self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_				:  str           =     3
        SCREAMING_SNAKE_CASE_				:  List[str]           =     input_dict['input_ids']
        SCREAMING_SNAKE_CASE_				:  List[str]           =     input_ids.ne(1					).to(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ids_tensor([self.model_tester.batch_size] ,					self.model_tester.type_sequence_label_size					)
        SCREAMING_SNAKE_CASE_				:  int           =     OpenLlamaForSequenceClassification(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  Dict           =     model(_SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE ,					labels=_SCREAMING_SNAKE_CASE					)
        self.assertEqual(result.logits.shape ,					(self.model_tester.batch_size, self.model_tester.num_labels)					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     3
        SCREAMING_SNAKE_CASE_				:  Tuple           =     'single_label_classification'
        SCREAMING_SNAKE_CASE_				:  int           =     input_dict['input_ids']
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     input_ids.ne(1					).to(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     ids_tensor([self.model_tester.batch_size] ,					self.model_tester.type_sequence_label_size					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     OpenLlamaForSequenceClassification(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     model(_SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE ,					labels=_SCREAMING_SNAKE_CASE					)
        self.assertEqual(result.logits.shape ,					(self.model_tester.batch_size, self.model_tester.num_labels)					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_				:  int           =     3
        SCREAMING_SNAKE_CASE_				:  Dict           =     'multi_label_classification'
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     input_dict['input_ids']
        SCREAMING_SNAKE_CASE_				:  Dict           =     input_ids.ne(1					).to(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     ids_tensor(
            [self.model_tester.batch_size, config.num_labels] ,					self.model_tester.type_sequence_label_size					).to(torch.float					)
        SCREAMING_SNAKE_CASE_				:  int           =     OpenLlamaForSequenceClassification(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  Dict           =     model(_SCREAMING_SNAKE_CASE ,					attention_mask=_SCREAMING_SNAKE_CASE ,					labels=_SCREAMING_SNAKE_CASE					)
        self.assertEqual(result.logits.shape ,					(self.model_tester.batch_size, self.model_tester.num_labels)					)

  @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'					)
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass



  @parameterized.expand([('linear',), ('dynamic',)]					)
  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     ids_tensor([1, 10] ,					config.vocab_size					)
        SCREAMING_SNAKE_CASE_				:  int           =     ids_tensor([1, int(config.max_position_embeddings * 1.5					)] ,					config.vocab_size					)

        set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     OpenLlamaModel(_SCREAMING_SNAKE_CASE					)
        original_model.to(_SCREAMING_SNAKE_CASE					)
        original_model.eval()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     original_model(_SCREAMING_SNAKE_CASE					).last_hidden_state
        SCREAMING_SNAKE_CASE_				:  Tuple           =     original_model(_SCREAMING_SNAKE_CASE					).last_hidden_state

        set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {'type': scaling_type, 'factor': 10.0}
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     OpenLlamaModel(_SCREAMING_SNAKE_CASE					)
        scaled_model.to(_SCREAMING_SNAKE_CASE					)
        scaled_model.eval()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scaled_model(_SCREAMING_SNAKE_CASE					).last_hidden_state
        SCREAMING_SNAKE_CASE_				:  Tuple           =     scaled_model(_SCREAMING_SNAKE_CASE					).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
              self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					atol=1e-5					)					)
        else:
              self.assertFalse(torch.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					atol=1e-5					)					)

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					atol=1e-5					)					)









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     :   Optional[int]						     =  logging.get_logger(__name__)


lowerCAmelCase     :   Optional[int]						     =  Dict[str, Any]
lowerCAmelCase     :   Any						     =  List[Prediction]
@add_end_docstrings(__magic_name__)
class  _A		(  __magic_name__):

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





        """simple docstring"""







        super().__init__(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        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 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     {}
        if "threshold" in kwargs:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs['threshold']
        return {}, {}, postprocess_kwargs

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





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     load_image(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.IntTensor([[image.height, image.width]]					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.image_processor(images=[image] ,					return_tensors='pt'					)
        if self.tokenizer is not None:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     self.tokenizer(text=inputs['words'] ,					boxes=inputs['boxes'] ,					return_tensors='pt'					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     target_size
        return inputs

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     model_inputs.pop('target_size'					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.model(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     outputs.__class__({'target_size': target_size, **outputs}					)
        if self.tokenizer is not None:
              SCREAMING_SNAKE_CASE_				:  str           =     model_inputs['bbox']
        return model_outputs

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0.9					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[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.
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     target_size[0].tolist()

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

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

              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scores.tolist()
              SCREAMING_SNAKE_CASE_				:  Any           =     [self.model.config.idalabel[label.item()] for label in labels]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [self._get_bounding_box(_SCREAMING_SNAKE_CASE					) for box in boxes]

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

        return annotation



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """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_				:  List[Any]           =     box.int().tolist()
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     {
            'xmin': xmin,
            'ymin': ymin,
            'xmax': xmax,
            'ymax': ymax,
        }
        return bbox






