






import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
     from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=13			,						a__=64			,						a__=2			,						a__=3			,						a__=True			,						a__=True			,						a__=32			,						a__=5			,						a__=4			,						a__=37			,						a__="gelu"			,						a__=0.1			,						a__=0.1			,						a__=10			,						a__=0.02			,						a__=[1, 16, 4, 4]			,						a__=None			,						) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						parent
            __snake_case			:Optional[int]          =						batch_size
            __snake_case			:Optional[Any]          =						image_size
            __snake_case			:str          =						patch_size
            __snake_case			:Optional[Any]          =						num_channels
            __snake_case			:str          =						is_training
            __snake_case			:Optional[Any]          =						use_labels
            __snake_case			:str          =						hidden_size
            __snake_case			:Union[str, Any]          =						num_hidden_layers
            __snake_case			:List[Any]          =						num_attention_heads
            __snake_case			:List[str]          =						intermediate_size
            __snake_case			:List[str]          =						hidden_act
            __snake_case			:List[str]          =						hidden_dropout_prob
            __snake_case			:Optional[int]          =						attention_probs_dropout_prob
            __snake_case			:Dict          =						type_sequence_label_size
            __snake_case			:List[str]          =						initializer_range
            __snake_case			:int          =						scope
            __snake_case			:Optional[int]          =						backbone_featmap_shape

            # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
            # the number of patches is based on the feature map of the backbone, which by default uses an output stride
            # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
            __snake_case			:int          =						(self.image_size // 32) ** 2
            __snake_case			:str          =						num_patches + 1



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

            __snake_case			:int          =						None
            if self.use_labels:
                 __snake_case			:List[Any]          =						ids_tensor([self.batch_size]			,						self.type_sequence_label_size    )

            __snake_case			:str          =						self.get_config()

            return config, pixel_values, labels



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:str          =						{
                """global_padding""": """same""",
                """layer_type""": """bottleneck""",
                """depths""": [3, 4, 9],
                """out_features""": ["""stage1""", """stage2""", """stage3"""],
                """embedding_dynamic_padding""": True,
                """hidden_sizes""": [4, 8, 16, 32],
                """num_groups""": 2,
            }

            return ViTHybridConfig(
                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=a__			,						initializer_range=self.initializer_range			,						backbone_featmap_shape=self.backbone_featmap_shape			,						backbone_config=a__			,						)



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      Any:






            '''simple docstring'''





            __snake_case			:int          =						ViTHybridModel(config=a__    )
            model.to(a__    )
            model.eval()
            __snake_case			:Tuple          =						model(a__    )
            self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.type_sequence_label_size
            __snake_case			:Optional[Any]          =						ViTHybridForImageClassification(a__    )
            model.to(a__    )
            model.eval()
            __snake_case			:int          =						model(a__			,						labels=a__    )
            self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.type_sequence_label_size)    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:str          =						self.prepare_config_and_inputs()
            __snake_case       , __snake_case       , __snake_case			:str          =						config_and_inputs
            __snake_case			:Tuple          =						{"""pixel_values""": pixel_values}
            return config, inputs_dict




@require_torch
class 			snake_case__   (							lowercase_	,    lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				List[Any] 				= (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
       lowerCamelCase							:				Tuple 				= (
           {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
           if is_torch_available()
           else {}
       )
       lowerCamelCase							:				List[str] 				= False
       lowerCamelCase							:				Optional[Any] 				= False
       lowerCamelCase							:				List[Any] 				= False



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:str          =						ViTHybridModelTester(self    )
            __snake_case			:List[str]          =						ConfigTester(self			,						config_class=a__			,						has_text_modality=a__			,						hidden_size=37    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            self.config_tester.run_common_tests()



       @unittest.skip(reason="""ViT does not use inputs_embeds"""    )
       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case       , __snake_case			:List[str]          =						self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case			:Dict          =						model_class(a__    )
                 self.assertIsInstance(model.get_input_embeddings()			,						(nn.Module)    )
                 __snake_case			:Optional[int]          =						model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(a__			,						nn.Linear    )    )



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __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			:str          =						model_class(a__    )
                 __snake_case			:Tuple          =						inspect.signature(model.forward    )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __snake_case			:List[Any]          =						[*signature.parameters.keys()]

                 __snake_case			:Optional[Any]          =						["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]			,						a__    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:str          =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a__    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Dict          =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*a__    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case       , __snake_case			:Any          =						self.model_tester.prepare_config_and_inputs_for_common()

            __snake_case			:Optional[Any]          =						_config_zero_init(a__    )
            for model_class in self.all_model_classes:
                 __snake_case			:Optional[Any]          =						model_class(config=a__    )
                 # Skip the check for the backbone
                 for name, module in model.named_modules():
                      if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                           __snake_case			:Optional[int]          =						[F'''{name}.{key}''' for key in module.state_dict().keys()]
                           break

                 for name, param in model.named_parameters():
                      if param.requires_grad:
                           if name in backbone_params:
                                continue
                           self.assertIn(
                               ((param.data.mean() * 1e9).round() / 1e9).item()			,						[0.0, 1.0]			,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,						)



       @slow
       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case			:Optional[Any]          =						ViTHybridModel.from_pretrained(a__    )
                 self.assertIsNotNone(a__    )






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Optional[Any]          =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
     return image




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

       '''simple docstring'''



       @cached_property
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            return (
                ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
                if is_vision_available()
                else None
            )



       @slow
       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:str          =						ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(
                a__    )

            __snake_case			:Any          =						self.default_image_processor
            __snake_case			:int          =						prepare_img()
            __snake_case			:Optional[int]          =						image_processor(images=a__			,						return_tensors="""pt"""    ).to(a__    )

            # forward pass
            with torch.no_grad():
                 __snake_case			:Optional[Any]          =						model(**a__    )

            # verify the logits
            __snake_case			:Optional[Any]          =						torch.Size((1, 10_00)    )
            self.assertEqual(outputs.logits.shape			,						a__    )

            __snake_case			:Dict          =						torch.tensor([-1.90_90, -0.49_93, -0.23_89]    ).to(a__    )

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



       @slow
       @require_accelerate
       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""    )
            __snake_case			:Optional[Any]          =						ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""			,						device_map="""auto"""    )

            __snake_case			:Dict          =						prepare_img()

            __snake_case			:str          =						image_processor(images=a__			,						return_tensors="""pt"""    )
            __snake_case			:Optional[Any]          =						model(**a__    )
            __snake_case			:List[str]          =						outputs.logits
            # model predicts one of the 1000 ImageNet classes
            __snake_case			:Optional[int]          =						logits.argmax(-1    ).item()

            self.assertTrue(model.config.idalabel[predicted_class_idx]			,						"""tabby, tabby cat"""    )










import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowerCamelCase__							=     """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""




class 			snake_case__   (							unittest.TestCase	,    lowercase_):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:int          =						load_tool("""text-question-answering"""    )
            self.tool.setup()
            __snake_case			:Tuple          =						load_tool("""text-question-answering"""			,						remote=a__    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:List[str]          =						self.tool(a__			,						"""What did Hugging Face do in April 2021?"""    )
            self.assertEqual(a__			,						"""launched the BigScience Research Workshop"""    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:List[str]          =						self.remote_tool(a__			,						"""What did Hugging Face do in April 2021?"""    )
            self.assertEqual(a__			,						"""launched the BigScience Research Workshop"""    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.tool(text=a__			,						question="""What did Hugging Face do in April 2021?"""    )
            self.assertEqual(a__			,						"""launched the BigScience Research Workshop"""    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:List[str]          =						self.remote_tool(text=a__			,						question="""What did Hugging Face do in April 2021?"""    )
            self.assertEqual(a__			,						"""launched the BigScience Research Workshop"""    )


