import inspect
import unittest

from transformers import YolosConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import YolosForObjectDetection, YolosModel
    from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase=13 , __lowercase=[30, 30] , __lowercase=2 , __lowercase=3 , __lowercase=True , __lowercase=True , __lowercase=32 , __lowercase=5 , __lowercase=4 , __lowercase=37 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=10 , __lowercase=0.02 , __lowercase=3 , __lowercase=None , __lowercase=8 , __lowercase=10 , )       ->	List[str]:
        __UpperCamelCase     :Optional[int]			  =		parent
        __UpperCamelCase     :List[str]			  =		batch_size
        __UpperCamelCase     :Tuple			  =		image_size
        __UpperCamelCase     :int			  =		patch_size
        __UpperCamelCase     :Union[str, Any]			  =		num_channels
        __UpperCamelCase     :Any			  =		is_training
        __UpperCamelCase     :Union[str, Any]			  =		use_labels
        __UpperCamelCase     :List[Any]			  =		hidden_size
        __UpperCamelCase     :Tuple			  =		num_hidden_layers
        __UpperCamelCase     :Tuple			  =		num_attention_heads
        __UpperCamelCase     :Union[str, Any]			  =		intermediate_size
        __UpperCamelCase     :Union[str, Any]			  =		hidden_act
        __UpperCamelCase     :int			  =		hidden_dropout_prob
        __UpperCamelCase     :Dict			  =		attention_probs_dropout_prob
        __UpperCamelCase     :Optional[int]			  =		type_sequence_label_size
        __UpperCamelCase     :List[str]			  =		initializer_range
        __UpperCamelCase     :Dict			  =		num_labels
        __UpperCamelCase     :Tuple			  =		scope
        __UpperCamelCase     :Optional[int]			  =		n_targets
        __UpperCamelCase     :Any			  =		num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        __UpperCamelCase     :str			  =		(image_size[1] // patch_size) * (image_size[0] // patch_size)
        __UpperCamelCase     :Dict			  =		num_patches + 1 + self.num_detection_tokens







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :List[Any]			  =		floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

        __UpperCamelCase     :Tuple			  =		None
        if self.use_labels:
            # labels is a list of Dict (each Dict being the labels for a given example in the batch)
            __UpperCamelCase     :List[Any]			  =		[]
            for i in range(self.batch_size):
                __UpperCamelCase     :Optional[Any]			  =		{}
                __UpperCamelCase     :str			  =		torch.randint(
                    high=self.num_labels , size=(self.n_targets,) , device=__lowercase)
                __UpperCamelCase     :Optional[Any]			  =		torch.rand(self.n_targets , 4 , device=__lowercase)
                labels.append(__lowercase)

        __UpperCamelCase     :Union[str, Any]			  =		self.get_config()

        return config, pixel_values, labels







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        return YolosConfig(
            image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=__lowercase , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :Optional[Any]			  =		YolosModel(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :Union[str, Any]			  =		model(__lowercase)
        self.parent.assertEqual(
            result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	int:
        __UpperCamelCase     :str			  =		YolosForObjectDetection(__lowercase)
        model.to(__lowercase)
        model.eval()

        __UpperCamelCase     :str			  =		model(pixel_values=__lowercase)
        __UpperCamelCase     :Optional[int]			  =		model(__lowercase)

        self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
        self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4))

        __UpperCamelCase     :Dict			  =		model(pixel_values=__lowercase , labels=__lowercase)

        self.parent.assertEqual(result.loss.shape , ())
        self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
        self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4))







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :int			  =		self.prepare_config_and_inputs()
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		config_and_inputs
        __UpperCamelCase     :Union[str, Any]			  =		{'''pixel_values''': pixel_values}
        return config, inputs_dict


@require_torch
class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Optional[int]						=      (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
    a__	:						Tuple						=      (
        {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
    )

    a__	:						Optional[Any]						=      False
    a__	:						Optional[Any]						=      False
    a__	:						Union[str, Any]						=      False
    a__	:						List[str]						=      False







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase=False)       ->	Tuple:
        __UpperCamelCase     :Optional[Any]			  =		super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase)

        if return_labels:
            if model_class.__name__ == "YolosForObjectDetection":
                __UpperCamelCase     :Dict			  =		[]
                for i in range(self.model_tester.batch_size):
                    __UpperCamelCase     :Union[str, Any]			  =		{}
                    __UpperCamelCase     :Tuple			  =		torch.ones(
                        size=(self.model_tester.n_targets,) , device=__lowercase , dtype=torch.long)
                    __UpperCamelCase     :str			  =		torch.ones(
                        self.model_tester.n_targets , 4 , device=__lowercase , dtype=torch.float)
                    labels.append(__lowercase)
                __UpperCamelCase     :Union[str, Any]			  =		labels

        return inputs_dict







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		YolosModelTester(self)
        __UpperCamelCase     :List[Any]			  =		ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37)







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        self.config_tester.run_common_tests()







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        # YOLOS does not use inputs_embeds
        pass







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Tuple			  =		model_class(__lowercase)
            self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
            __UpperCamelCase     :Tuple			  =		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__lowercase , nn.Linear))







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCamelCase     :Tuple			  =		['''pixel_values''']
            self.assertListEqual(arg_names[:1] , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :List[Any]			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase     :str			  =		True

        # in YOLOS, the seq_len is different
        __UpperCamelCase     :List[str]			  =		self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
            __UpperCamelCase     :Dict			  =		True
            __UpperCamelCase     :List[Any]			  =		False
            __UpperCamelCase     :Optional[Any]			  =		True
            __UpperCamelCase     :List[Any]			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()
            with torch.no_grad():
                __UpperCamelCase     :Optional[Any]			  =		model(**self._prepare_for_class(__lowercase , __lowercase))
            __UpperCamelCase     :List[str]			  =		outputs.attentions
            self.assertEqual(len(__lowercase) , self.model_tester.num_hidden_layers)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            __UpperCamelCase     :Optional[Any]			  =		True
            __UpperCamelCase     :List[str]			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()
            with torch.no_grad():
                __UpperCamelCase     :Optional[Any]			  =		model(**self._prepare_for_class(__lowercase , __lowercase))
            __UpperCamelCase     :List[str]			  =		outputs.attentions
            self.assertEqual(len(__lowercase) , self.model_tester.num_hidden_layers)

            self.assertListEqual(
                list(attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
            __UpperCamelCase     :int			  =		len(__lowercase)

            # Check attention is always last and order is fine
            __UpperCamelCase     :Any			  =		True
            __UpperCamelCase     :List[Any]			  =		True
            __UpperCamelCase     :Dict			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()
            with torch.no_grad():
                __UpperCamelCase     :int			  =		model(**self._prepare_for_class(__lowercase , __lowercase))

            __UpperCamelCase     :List[Any]			  =		1
            self.assertEqual(out_len + added_hidden_states , len(__lowercase))

            __UpperCamelCase     :Union[str, Any]			  =		outputs.attentions

            self.assertEqual(len(__lowercase) , self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        def check_hidden_states_output(__lowercase , __lowercase , __lowercase):
            __UpperCamelCase     :List[Any]			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()

            with torch.no_grad():
                __UpperCamelCase     :Optional[Any]			  =		model(**self._prepare_for_class(__lowercase , __lowercase))

            __UpperCamelCase     :Any			  =		outputs.hidden_states

            __UpperCamelCase     :Any			  =		getattr(
                self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1)
            self.assertEqual(len(__lowercase) , __lowercase)

            # YOLOS has a different seq_length
            __UpperCamelCase     :List[str]			  =		self.model_tester.expected_seq_len

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]) , [seq_length, self.model_tester.hidden_size] , )

        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Tuple			  =		True
            check_hidden_states_output(__lowercase , __lowercase , __lowercase)

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

            check_hidden_states_output(__lowercase , __lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Tuple			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*__lowercase)







    @slow
    def 			UpperCamelCase__   (	self)       ->	Any:
        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase     :Any			  =		YolosModel.from_pretrained(__lowercase)
            self.assertIsNotNone(__lowercase)



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image


@require_torch
@require_vision
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    @cached_property
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        return AutoImageProcessor.from_pretrained('''hustvl/yolos-small''') if is_vision_available() else None







    @slow
    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :List[str]			  =		YolosForObjectDetection.from_pretrained('''hustvl/yolos-small''').to(__lowercase)

        __UpperCamelCase     :Dict			  =		self.default_image_processor
        __UpperCamelCase     :Optional[int]			  =		prepare_img()
        __UpperCamelCase     :Any			  =		image_processor(images=__lowercase , return_tensors='''pt''').to(__lowercase)

        # forward pass
        with torch.no_grad():
            __UpperCamelCase     :Optional[int]			  =		model(inputs.pixel_values)

        # verify outputs
        __UpperCamelCase     :str			  =		torch.Size((1, 100, 92))
        self.assertEqual(outputs.logits.shape , __lowercase)

        __UpperCamelCase     :int			  =		torch.tensor(
            [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]] , device=__lowercase , )
        __UpperCamelCase     :Any			  =		torch.tensor(
            [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]] , device=__lowercase)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , __lowercase , atol=1E-4))
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , __lowercase , atol=1E-4))

        # verify postprocessing
        __UpperCamelCase     :Tuple			  =		image_processor.post_process_object_detection(
            __lowercase , threshold=0.3 , target_sizes=[image.size[::-1]])[0]
        __UpperCamelCase     :Optional[int]			  =		torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]).to(__lowercase)
        __UpperCamelCase     :Optional[int]			  =		[75, 75, 17, 63, 17]
        __UpperCamelCase     :int			  =		torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]).to(__lowercase)

        self.assertEqual(len(results['''scores''']) , 5)
        self.assertTrue(torch.allclose(results['''scores'''] , __lowercase , atol=1E-4))
        self.assertSequenceEqual(results['''labels'''].tolist() , __lowercase)
        self.assertTrue(torch.allclose(results['''boxes'''][0, :] , __lowercase))







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      lowercase__       =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
