






import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
       from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image







class  _lowercase   :
      def __init__(     self				,			a				,			a=3				,			a=3_2				,			a=3				,			a=1_0				,			a=[8, 1_6, 3_2, 6_4]				,			a=[1, 1, 2, 1]				,			a=True				,			a=True				,			a="relu"				,			a=3				,			a=None				,			a=["stage2", "stage3", "stage4"]				,			a=[2, 3, 4]				,			a=1				,			):
       snake_case__  :    List[str]							       =parent
       snake_case__  :    Dict							       =batch_size
       snake_case__  :    Optional[int]							       =image_size
       snake_case__  :    str							       =num_channels
       snake_case__  :    Any							       =embeddings_size
       snake_case__  :    Any							       =hidden_sizes
       snake_case__  :    List[str]							       =depths
       snake_case__  :    List[Any]							       =is_training
       snake_case__  :    Dict							       =use_labels
       snake_case__  :    Dict							       =hidden_act
       snake_case__  :    str							       =num_labels
       snake_case__  :    Dict							       =scope
       snake_case__  :    str							       =len(a   )
       snake_case__  :    Optional[Any]							       =out_features
       snake_case__  :    List[str]							       =out_indices
       snake_case__  :    Union[str, Any]							       =num_groups
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       snake_case__  :    Optional[int]							       =None
       if self.use_labels:
        snake_case__  :    Union[str, Any]							       =ids_tensor([self.batch_size]				,			self.num_labels   )

       snake_case__  :    Any							       =self.get_config()

       return config, pixel_values, labels
      def 						lowercase__					(     self   ):
       return BitConfig(
           num_channels=self.num_channels				,			embeddings_size=self.embeddings_size				,			hidden_sizes=self.hidden_sizes				,			depths=self.depths				,			hidden_act=self.hidden_act				,			num_labels=self.num_labels				,			out_features=self.out_features				,			out_indices=self.out_indices				,			num_groups=self.num_groups				,			)
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Optional[int]							       =BitModel(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    List[Any]							       =model(a   )
       self.parent.assertEqual(
           result.last_hidden_state.shape				,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)				,			)
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    int							       =self.num_labels
       snake_case__  :    int							       =BitForImageClassification(a   )
       model.to(a   )
       model.eval()
       snake_case__  :    Optional[Any]							       =model(a				,			labels=a   )
       self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Dict							       =BitBackbone(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    List[Any]							       =model(a   )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps   )				,			len(config.out_features   )   )
       self.parent.assertListEqual(list(result.feature_maps[0].shape   )				,			[self.batch_size, self.hidden_sizes[1], 4, 4]   )

       # verify channels
       self.parent.assertEqual(len(model.channels   )				,			len(config.out_features   )   )
       self.parent.assertListEqual(model.channels				,			config.hidden_sizes[1:]   )

       # verify backbone works with out_features=None
       snake_case__  :    Dict							       =None
       snake_case__  :    Optional[int]							       =BitBackbone(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    int							       =model(a   )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps   )				,			1   )
       self.parent.assertListEqual(list(result.feature_maps[0].shape   )				,			[self.batch_size, self.hidden_sizes[-1], 1, 1]   )

       # verify channels
       self.parent.assertEqual(len(model.channels   )				,			1   )
       self.parent.assertListEqual(model.channels				,			[config.hidden_sizes[-1]]   )



      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =self.prepare_config_and_inputs()
       snake_case__       ,   snake_case__       ,   snake_case__  :    Dict							       =config_and_inputs
       snake_case__  :    List[Any]							       ={"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class  _lowercase   (   _A						,				_A						,				unittest.TestCase						):
      _a							:						Tuple										=							(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      _a							:						Dict										=							(
          {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
          if is_torch_available()
          else {}
      )

      _a							:						Any										=							False
      _a							:						List[str]										=							False
      _a							:						List[str]										=							False
      _a							:						Dict										=							False
      _a							:						Optional[Any]										=							False
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =BitModelTester(self   )
       snake_case__  :    Tuple							       =ConfigTester(self				,			config_class=a				,			has_text_modality=a   )
      def 						lowercase__					(     self   ):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()
      def 						lowercase__					(     self   ):
       return
      @unittest.skip(reason="""Bit does not output attentions"""   )
      def 						lowercase__					(     self   ):
       pass
      @unittest.skip(reason="""Bit does not use inputs_embeds"""   )
      def 						lowercase__					(     self   ):
       pass
      @unittest.skip(reason="""Bit does not support input and output embeddings"""   )
      def 						lowercase__					(     self   ):
       pass
      def 						lowercase__					(     self   ):
       snake_case__       ,   snake_case__  :    str							       =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        snake_case__  :    Dict							       =model_class(a   )
        snake_case__  :    Dict							       =inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        snake_case__  :    str							       =[*signature.parameters.keys()]

        snake_case__  :    Dict							       =["""pixel_values"""]
        self.assertListEqual(arg_names[:1]				,			a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*a   )
      def 						lowercase__					(     self   ):
       snake_case__       ,   snake_case__  :    Optional[int]							       =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        snake_case__  :    Optional[int]							       =model_class(config=a   )
        for name, module in model.named_modules():
         if isinstance(a				,			(nn.BatchNormad, nn.GroupNorm)   ):
          self.assertTrue(
              torch.all(module.weight == 1   )				,			msg=F"Parameter {name} of model {model_class} seems not properly initialized"				,			)
          self.assertTrue(
              torch.all(module.bias == 0   )				,			msg=F"Parameter {name} of model {model_class} seems not properly initialized"				,			)
      def 						lowercase__					(     self   ):
       def check_hidden_states_output(a				,			a				,			a   ):
        snake_case__  :    Optional[Any]							       =model_class(a   )
        model.to(a   )
        model.eval()

        with torch.no_grad():
         snake_case__  :    List[Any]							       =model(**self._prepare_for_class(a				,			a   )   )

        snake_case__  :    Tuple							       =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        snake_case__  :    Tuple							       =self.model_tester.num_stages
        self.assertEqual(len(a   )				,			expected_num_stages + 1   )

        # Bit's feature maps are of shape (batch_size, num_channels, height, width)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:]   )				,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,			)

       snake_case__       ,   snake_case__  :    Tuple							       =self.model_tester.prepare_config_and_inputs_for_common()
       snake_case__  :    int							       =["""preactivation""", """bottleneck"""]
       for model_class in self.all_model_classes:
        for layer_type in layers_type:
         snake_case__  :    str							       =layer_type
         snake_case__  :    Any							       =True
         check_hidden_states_output(a				,			a				,			a   )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case__  :    Tuple							       =True

         check_hidden_states_output(a				,			a				,			a   )
      @unittest.skip(reason="""Bit does not use feedforward chunking"""   )
      def 						lowercase__					(     self   ):
       pass
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a   )



      @slow
      def 						lowercase__					(     self   ):
       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case__  :    Optional[int]							       =BitModel.from_pretrained(a   )
        self.assertIsNotNone(a   )







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Tuple							       =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
 return image







@require_torch
@require_vision
class  _lowercase   (   unittest.TestCase						):
      @cached_property
      def 						lowercase__					(     self   ):
       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
       )



      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(a   )

       snake_case__  :    Any							       =self.default_image_processor
       snake_case__  :    int							       =prepare_img()
       snake_case__  :    str							       =image_processor(images=a				,			return_tensors="""pt"""   ).to(a   )

       # forward pass
       with torch.no_grad():
        snake_case__  :    Optional[int]							       =model(**a   )

       # verify the logits
       snake_case__  :    str							       =torch.Size((1, 1_0_0_0)   )
       self.assertEqual(outputs.logits.shape				,			a   )

       snake_case__  :    int							       =torch.tensor([[-0.6526, -0.5263, -1.4398]]   ).to(a   )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,			a				,			atol=1e-4   )   )







@require_torch
class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						str										=							(BitBackbone,) if is_torch_available() else ()
      _a							:						List[Any]										=							BitConfig

      _a							:						Optional[int]										=							False
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =BitModelTester(self   )













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, ChunkPipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from transformers.modeling_outputs import BaseModelOutput

       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)







@add_end_docstrings(_A						)
class  _lowercase   (   _A						):
      def __init__(     self				,			**a   ):
       super().__init__(**a   )

       if self.framework == "tf":
        raise ValueError(F"The {self.__class__} is only available in PyTorch."   )

       requires_backends(self				,			"""vision"""   )
       self.check_model_type(a   )
      def __call__(     self				,			a				,			a = None				,			**a				,			):
       if "text_queries" in kwargs:
        snake_case__  :    Any							       =kwargs.pop("""text_queries"""   )

       if isinstance(a				,			(str, Image.Image)   ):
        snake_case__  :    Union[str, Any]							       ={"""image""": image, """candidate_labels""": candidate_labels}
       else:
        snake_case__  :    str							       =image
       snake_case__  :    Union[str, Any]							       =super().__call__(a				,			**a   )
       return results
      def 						lowercase__					(     self				,			**a   ):
       snake_case__  :    Union[str, Any]							       ={}
       if "threshold" in kwargs:
        snake_case__  :    Optional[Any]							       =kwargs["""threshold"""]
       if "top_k" in kwargs:
        snake_case__  :    Dict							       =kwargs["""top_k"""]
       return {}, {}, postprocess_params
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    List[Any]							       =load_image(inputs["""image"""]   )
       snake_case__  :    Dict							       =inputs["""candidate_labels"""]
       if isinstance(a				,			a   ):
        snake_case__  :    str							       =candidate_labels.split(""","""   )

       snake_case__  :    Optional[Any]							       =torch.tensor([[image.height, image.width]]				,			dtype=torch.intaa   )
       for i, candidate_label in enumerate(a   ):
        snake_case__  :    Tuple							       =self.tokenizer(a				,			return_tensors=self.framework   )
        snake_case__  :    int							       =self.image_processor(a				,			return_tensors=self.framework   )
        yield {
            "is_last": i == len(a   ) - 1,
            "target_size": target_size,
            "candidate_label": candidate_label,
            **text_inputs,
            **image_features,
        }
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Tuple							       =model_inputs.pop("""target_size"""   )
       snake_case__  :    Tuple							       =model_inputs.pop("""candidate_label"""   )
       snake_case__  :    Dict							       =model_inputs.pop("""is_last"""   )

       snake_case__  :    Any							       =self.model(**a   )

       snake_case__  :    List[Any]							       ={"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
       return model_outputs
      def 						lowercase__					(     self				,			a				,			a=0.1				,			a=None   ):
       snake_case__  :    Optional[int]							       =[]
       for model_output in model_outputs:
        snake_case__  :    Tuple							       =model_output["""candidate_label"""]
        snake_case__  :    Optional[int]							       =BaseModelOutput(a   )
        snake_case__  :    int							       =self.image_processor.post_process_object_detection(
            outputs=a				,			threshold=a				,			target_sizes=model_output["""target_size"""]   )[0]

        for index in outputs["scores"].nonzero():
         snake_case__  :    int							       =outputs["""scores"""][index].item()
         snake_case__  :    Tuple							       =self._get_bounding_box(outputs["""boxes"""][index][0]   )

         snake_case__  :    Tuple							       ={"""score""": score, """label""": label, """box""": box}
         results.append(a   )

       snake_case__  :    Tuple							       =sorted(a				,			key=lambda a   : x["score"]				,			reverse=a   )
       if top_k:
        snake_case__  :    Union[str, Any]							       =results[:top_k]

       return results



      def 						lowercase__					(     self				,			a   ):
       if self.framework != "pt":
        raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""   )
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Dict							       =box.int().tolist()
       snake_case__  :    int							       ={
           """xmin""": xmin,
           """ymin""": ymin,
           """xmax""": xmax,
           """ymax""": ymax,
       }
       return bbox





