






"""simple docstring"""



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    lowercase__			:


			def __init__(      self     :  List[str]   ,       snake_case__     :  Optional[int]   ,       snake_case__     :  List[str]=13   ,       snake_case__     :  List[Any]=64   ,       snake_case__     :  Optional[int]=2   ,       snake_case__     :  List[str]=3   ,       snake_case__     :  int=True   ,       snake_case__     :  Optional[int]=True   ,       snake_case__     :  List[Any]=32   ,       snake_case__     :  Any=5   ,       snake_case__     :  List[str]=4   ,       snake_case__     :  Any=37   ,       snake_case__     :  Dict="gelu"   ,       snake_case__     :  List[Any]=0.1   ,       snake_case__     :  Union[str, Any]=0.1   ,       snake_case__     :  int=10   ,       snake_case__     :  Tuple=0.02   ,       snake_case__     :  Tuple=[1, 16, 4, 4]   ,       snake_case__     :  Any=None   ,       ):
										lowerCamelCase_       :							Optional[Any]									=parent
										lowerCamelCase_       :							Dict									=batch_size
										lowerCamelCase_       :							str									=image_size
										lowerCamelCase_       :							Tuple									=patch_size
										lowerCamelCase_       :							Any									=num_channels
										lowerCamelCase_       :							str									=is_training
										lowerCamelCase_       :							List[str]									=use_labels
										lowerCamelCase_       :							Union[str, Any]									=hidden_size
										lowerCamelCase_       :							Optional[Any]									=num_hidden_layers
										lowerCamelCase_       :							Dict									=num_attention_heads
										lowerCamelCase_       :							List[Any]									=intermediate_size
										lowerCamelCase_       :							str									=hidden_act
										lowerCamelCase_       :							List[Any]									=hidden_dropout_prob
										lowerCamelCase_       :							int									=attention_probs_dropout_prob
										lowerCamelCase_       :							Union[str, Any]									=type_sequence_label_size
										lowerCamelCase_       :							Optional[Any]									=initializer_range
										lowerCamelCase_       :							List[Any]									=scope
										lowerCamelCase_       :							Any									=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
										lowerCamelCase_       :							Tuple									=(self.image_size // 32) ** 2
										lowerCamelCase_       :							Dict									=num_patches + 1


			def 	UpperCAmelCase__						(      self     :  int					):
										lowerCamelCase_       :							List[str]									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										lowerCamelCase_       :							str									=None
										if self.use_labels:
																	lowerCamelCase_       :							Union[str, Any]									=ids_tensor([self.batch_size]   ,       self.type_sequence_label_size					)

										lowerCamelCase_       :							Optional[int]									=self.get_config()

										return config, pixel_values, labels


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							List[Any]									={
										    "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=UpperCAmelCase__   ,       initializer_range=self.initializer_range   ,       backbone_featmap_shape=self.backbone_featmap_shape   ,       backbone_config=UpperCAmelCase__   ,       )


			def 	UpperCAmelCase__						(      self     :  Optional[int]   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  List[Any]   ,       snake_case__     :  int					):
										lowerCamelCase_       :							str									=ViTHybridModel(config=UpperCAmelCase__					)
										model.to(UpperCAmelCase__					)
										model.eval()
										lowerCamelCase_       :							Any									=model(UpperCAmelCase__					)
										self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)					)


			def 	UpperCAmelCase__						(      self     :  Tuple   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  Optional[Any]					):
										lowerCamelCase_       :							Tuple									=self.type_sequence_label_size
										lowerCamelCase_       :							Any									=ViTHybridForImageClassification(UpperCAmelCase__					)
										model.to(UpperCAmelCase__					)
										model.eval()
										lowerCamelCase_       :							Optional[Any]									=model(UpperCAmelCase__   ,       labels=UpperCAmelCase__					)
										self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.type_sequence_label_size)					)


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_       :							Optional[int]									=self.prepare_config_and_inputs()
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_       :							Optional[Any]									=config_and_inputs
										lowerCamelCase_       :							str									={"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class    lowercase__			(							UpperCAmelCase__,   UpperCAmelCase__,   unittest.TestCase   ):
			_UpperCAmelCase		:Optional[int]   			= (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
			_UpperCAmelCase		:int   			= (
			    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
			    if is_torch_available()
			    else {}
			)
			_UpperCAmelCase		:str   			= False
			_UpperCAmelCase		:List[str]   			= False
			_UpperCAmelCase		:str   			= False


			def 	UpperCAmelCase__						(      self     :  Dict					):
										lowerCamelCase_       :							Optional[int]									=ViTHybridModelTester(self					)
										lowerCamelCase_       :							Any									=ConfigTester(self   ,       config_class=UpperCAmelCase__   ,       has_text_modality=UpperCAmelCase__   ,       hidden_size=37					)


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										self.config_tester.run_common_tests()


			@unittest.skip(reason="ViT does not use inputs_embeds"					)
			def 	UpperCAmelCase__						(      self     :  str					):
										pass


			def 	UpperCAmelCase__						(      self     :  str					):
										lowerCamelCase_							,						lowerCamelCase_       :							Dict									=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowerCamelCase_       :							List[Any]									=model_class(UpperCAmelCase__					)
																	self.assertIsInstance(model.get_input_embeddings()   ,       (nn.Module)					)
																	lowerCamelCase_       :							Optional[Any]									=model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(UpperCAmelCase__   ,       nn.Linear					)					)


			def 	UpperCAmelCase__						(      self     :  List[Any]					):
										lowerCamelCase_							,						lowerCamelCase_       :							List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowerCamelCase_       :							Dict									=model_class(UpperCAmelCase__					)
																	lowerCamelCase_       :							Union[str, Any]									=inspect.signature(model.forward					)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowerCamelCase_       :							Tuple									=[*signature.parameters.keys()]

																	lowerCamelCase_       :							Union[str, Any]									=["pixel_values"]
																	self.assertListEqual(arg_names[:1]   ,       UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  int					):
										lowerCamelCase_       :							Dict									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										lowerCamelCase_       :							str									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_							,						lowerCamelCase_       :							str									=self.model_tester.prepare_config_and_inputs_for_common()

										lowerCamelCase_       :							int									=_config_zero_init(UpperCAmelCase__					)
										for model_class in self.all_model_classes:
																	lowerCamelCase_       :							Dict									=model_class(config=UpperCAmelCase__					)
																	# Skip the check for the backbone
																	for name, module in model.named_modules():
																								if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																															lowerCamelCase_       :							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 	UpperCAmelCase__						(      self     :  Optional[int]					):

										for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCamelCase_       :							str									=ViTHybridModel.from_pretrained(UpperCAmelCase__					)
																	self.assertIsNotNone(UpperCAmelCase__					)


def  _snake_case				(     )				->	List[str]:
							lowerCamelCase_       :							int									=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
							return image







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


			@cached_property
			def 	UpperCAmelCase__						(      self     :  List[str]					):
										return (
										    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
										    if is_vision_available()
										    else None
										)


			@slow
			def 	UpperCAmelCase__						(      self     :  Tuple					):
										lowerCamelCase_       :							Optional[Any]									=ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(
										    UpperCAmelCase__					)

										lowerCamelCase_       :							Union[str, Any]									=self.default_image_processor
										lowerCamelCase_       :							Union[str, Any]									=prepare_img()
										lowerCamelCase_       :							List[str]									=image_processor(images=UpperCAmelCase__   ,       return_tensors="pt"					).to(UpperCAmelCase__					)

										# forward pass
										with torch.no_grad():
																	lowerCamelCase_       :							Union[str, Any]									=model(**UpperCAmelCase__					)

										# verify the logits
										lowerCamelCase_       :							Optional[Any]									=torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape   ,       UpperCAmelCase__					)

										lowerCamelCase_       :							Dict									=torch.tensor([-1.9_090, -0.4_993, -0.2_389]					).to(UpperCAmelCase__					)

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


			@slow
			@require_accelerate
			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										lowerCamelCase_       :							List[Any]									=ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"					)
										lowerCamelCase_       :							Any									=ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"   ,       device_map="auto"					)

										lowerCamelCase_       :							Dict									=prepare_img()

										lowerCamelCase_       :							Union[str, Any]									=image_processor(images=UpperCAmelCase__   ,       return_tensors="pt"					)
										lowerCamelCase_       :							Tuple									=model(**UpperCAmelCase__					)
										lowerCamelCase_       :							Tuple									=outputs.logits
										# model predicts one of the 1000 ImageNet classes
										lowerCamelCase_       :							str									=logits.argmax(-1					).item()

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









from ...processing_utils import ProcessorMixin


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''SpeechT5FeatureExtractor'''
    UpperCAmelCase__							  =      '''SpeechT5Tokenizer'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Tuple)						->Union[str, Any]:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__					, UpperCAmelCase__)


    def __call__(			self      :	Dict					, *UpperCAmelCase__      :	List[str]					, **UpperCAmelCase__      :	Any)						->Optional[Any]:





           '''simple docstring'''

           A__       =    kwargs.pop('''audio'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''audio_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''sampling_rate'''					, UpperCAmelCase__)

           if audio is not None and text is not None:
                  raise ValueError(
                      '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?''')
           if audio_target is not None and text_target is not None:
                  raise ValueError(
                      '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?''')
           if audio is None and audio_target is None and text is None and text_target is None:
                  raise ValueError(
                      '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.''')

           if audio is not None:
                  A__       =    self.feature_extractor(UpperCAmelCase__					, *UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, **UpperCAmelCase__)
           elif text is not None:
                  A__       =    self.tokenizer(UpperCAmelCase__					, **UpperCAmelCase__)
           else:
                  A__       =    None

           if audio_target is not None:
                  A__       =    self.feature_extractor(audio_target=UpperCAmelCase__					, *UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, **UpperCAmelCase__)
                  A__       =    targets['''input_values''']
           elif text_target is not None:
                  A__       =    self.tokenizer(UpperCAmelCase__					, **UpperCAmelCase__)
                  A__       =    targets['''input_ids''']
           else:
                  A__       =    None

           if inputs is None:
                  return targets

           if targets is not None:
                  A__       =    labels

                  A__       =    targets.get('''attention_mask''')
                  if decoder_attention_mask is not None:
                         A__       =    decoder_attention_mask

           return inputs


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	Tuple					, **UpperCAmelCase__      :	int)						->Optional[int]:





           '''simple docstring'''

           A__       =    kwargs.pop('''input_values'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''input_ids'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''labels'''					, UpperCAmelCase__)

           if input_values is not None and input_ids is not None:
                  raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.''')
           if input_values is None and input_ids is None and labels is None:
                  raise ValueError(
                      '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.''')

           if input_values is not None:
                  A__       =    self.feature_extractor.pad(UpperCAmelCase__					, *UpperCAmelCase__					, **UpperCAmelCase__)
           elif input_ids is not None:
                  A__       =    self.tokenizer.pad(UpperCAmelCase__					, **UpperCAmelCase__)
           else:
                  A__       =    None

           if labels is not None:
                  if "input_ids" in labels or (isinstance(UpperCAmelCase__					, UpperCAmelCase__) and "input_ids" in labels[0]):
                         A__       =    self.tokenizer.pad(UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    targets['''input_ids''']
                  else:
                         A__       =    self.feature_extractor.feature_size
                         A__       =    self.feature_extractor.num_mel_bins
                         A__       =    self.feature_extractor.pad(UpperCAmelCase__					, *UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    feature_size_hack
                         A__       =    targets['''input_values''']
           else:
                  A__       =    None

           if inputs is None:
                  return targets

           if targets is not None:
                  A__       =    labels

                  A__       =    targets.get('''attention_mask''')
                  if decoder_attention_mask is not None:
                         A__       =    decoder_attention_mask

           return inputs


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, *UpperCAmelCase__      :	Dict					, **UpperCAmelCase__      :	Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           return self.tokenizer.batch_decode(*UpperCAmelCase__					, **UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Union[str, Any])						->Dict:





           '''simple docstring'''

           return self.tokenizer.decode(*UpperCAmelCase__					, **UpperCAmelCase__)


