






import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
       MODEL_MAPPING,
       DataaVecVisionForImageClassification,
       DataaVecVisionForSemanticSegmentation,
       DataaVecVisionModel,
   )
   from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor

class 					snake_case_							:







   """simple docstring"""



   def __init__(    self:	Union[str, Any]   ,       snake_case__:	List[str]   ,       snake_case__:	Dict=100   ,       snake_case__:	str=13   ,       snake_case__:	List[Any]=30   ,       snake_case__:	List[Any]=2   ,       snake_case__:	Union[str, Any]=3   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[str]=True   ,       snake_case__:	str=32   ,       snake_case__:	List[Any]=4   ,       snake_case__:	Tuple=4   ,       snake_case__:	Optional[Any]=37   ,       snake_case__:	Dict="gelu"   ,       snake_case__:	Any=0.1   ,       snake_case__:	List[Any]=0.1   ,       snake_case__:	int=10   ,       snake_case__:	List[str]=0.02   ,       snake_case__:	Dict=3   ,       snake_case__:	str=None   ,       snake_case__:	Union[str, Any]=[0, 1, 2, 3]   ,       )  ->		List[Any]:


         """simple docstring"""



         _lowercase     =	parent
         _lowercase     =	100
         _lowercase     =	batch_size
         _lowercase     =	image_size
         _lowercase     =	patch_size
         _lowercase     =	num_channels
         _lowercase     =	is_training
         _lowercase     =	use_labels
         _lowercase     =	hidden_size
         _lowercase     =	num_hidden_layers
         _lowercase     =	num_attention_heads
         _lowercase     =	intermediate_size
         _lowercase     =	hidden_act
         _lowercase     =	hidden_dropout_prob
         _lowercase     =	attention_probs_dropout_prob
         _lowercase     =	type_sequence_label_size
         _lowercase     =	initializer_range
         _lowercase     =	scope
         _lowercase     =	out_indices
         _lowercase     =	num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         _lowercase     =	(image_size // patch_size) ** 2
         _lowercase     =	num_patches + 1

   def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		List[str]:


         """simple docstring"""



         _lowercase     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         _lowercase     =	None
         _lowercase     =	None
         if self.use_labels:
               _lowercase     =	ids_tensor([self.batch_size]   ,       self.type_sequence_label_size	)
               _lowercase     =	ids_tensor([self.batch_size, self.image_size, self.image_size]   ,       self.num_labels	)

         _lowercase     =	self.get_config()

         return config, pixel_values, labels, pixel_labels

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Dict:


         """simple docstring"""



         return DataaVecVisionConfig(
             vocab_size=self.vocab_size   ,       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=snake_case__   ,       initializer_range=self.initializer_range   ,       out_indices=self.out_indices   ,       )

   def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	List[Any]   ,       snake_case__:	List[str]   ,       snake_case__:	Any   ,       snake_case__:	List[Any]	)  ->		Union[str, Any]:


         """simple docstring"""



         _lowercase     =	DataaVecVisionModel(config=snake_case__	)
         model.to(snake_case__	)
         model.eval()
         _lowercase     =	model(snake_case__	)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         _lowercase     =	(self.image_size // self.patch_size) ** 2
         self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, num_patches + 1, self.hidden_size)	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	Tuple   ,       snake_case__:	List[str]   ,       snake_case__:	Any   ,       snake_case__:	Dict	)  ->		Optional[Any]:


         """simple docstring"""



         _lowercase     =	self.type_sequence_label_size
         _lowercase     =	DataaVecVisionForImageClassification(snake_case__	)
         model.to(snake_case__	)
         model.eval()
         _lowercase     =	model(snake_case__   ,       labels=snake_case__	)
         self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.type_sequence_label_size)	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	Optional[int]   ,       snake_case__:	Dict   ,       snake_case__:	str	)  ->		Dict:


         """simple docstring"""



         _lowercase     =	self.num_labels
         _lowercase     =	DataaVecVisionForSemanticSegmentation(snake_case__	)
         model.to(snake_case__	)
         model.eval()
         _lowercase     =	model(snake_case__	)
         self.parent.assertEqual(
             result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
         _lowercase     =	model(snake_case__   ,       labels=snake_case__	)
         self.parent.assertEqual(
             result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Dict:


         """simple docstring"""



         _lowercase     =	self.prepare_config_and_inputs()
         _lowercase ,    _lowercase ,    _lowercase ,    _lowercase     =	config_and_inputs
         _lowercase     =	{'pixel_values': pixel_values}
         return config, inputs_dict






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







   """simple docstring"""



   __lowerCAmelCase      =						(
       (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase      =						(
       {
           """feature-extraction""": DataaVecVisionModel,
           """image-classification""": DataaVecVisionForImageClassification,
           """image-segmentation""": DataaVecVisionForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase      =						False
   __lowerCAmelCase      =						False
   __lowerCAmelCase      =						False

   def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		str:


         """simple docstring"""



         _lowercase     =	DataaVecVisionModelTester(self	)
         _lowercase     =	ConfigTester(
             self   ,       config_class=snake_case__   ,       has_text_modality=snake_case__   ,       hidden_size=37	)

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


         """simple docstring"""



         self.config_tester.run_common_tests()

   def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		List[Any]:


         """simple docstring"""



         pass

   @require_torch_multi_gpu
   @unittest.skip(
       reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'	)
   def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Optional[Any]:


         """simple docstring"""



         pass

   def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Optional[int]:


         """simple docstring"""



         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()

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

   def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		int:


         """simple docstring"""



         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()

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

               _lowercase     =	['pixel_values']
               self.assertListEqual(arg_names[:1]   ,       snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		str:


         """simple docstring"""



         _lowercase     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Union[str, Any]:


         """simple docstring"""



         _lowercase     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		Dict:


         """simple docstring"""



         if not self.model_tester.is_training:
               return

         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
         _lowercase     =	True

         for model_class in self.all_model_classes:
               if model_class in [*get_values(snake_case__	)]:
                     continue

               _lowercase     =	model_class(snake_case__	)
               model.to(snake_case__	)
               model.train()
               _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__	)
               _lowercase     =	model(**snake_case__	).loss
               loss.backward()

   def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Dict:


         """simple docstring"""



         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
               return

         _lowercase     =	False
         _lowercase     =	True

         for model_class in self.all_model_classes:
               if model_class in [*get_values(snake_case__	)] or not model_class.supports_gradient_checkpointing:
                     continue
               # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
               # this can then be incorporated into _prepare_for_class in test_modeling_common.py
               elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                     _lowercase ,    _lowercase ,    _lowercase ,    _lowercase     =	inputs_dict['pixel_values'].shape
                     _lowercase     =	torch.zeros(
                         [self.model_tester.batch_size, height, width]   ,       device=snake_case__	).long()
               _lowercase     =	model_class(snake_case__	)
               model.gradient_checkpointing_enable()
               model.to(snake_case__	)
               model.train()
               _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__	)
               _lowercase     =	model(**snake_case__	).loss
               loss.backward()

   def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		List[str]:


         """simple docstring"""



         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()

         _lowercase     =	_config_zero_init(snake_case__	)
         for model_class in self.all_model_classes:
               _lowercase     =	model_class(config=snake_case__	)
               for name, param in model.named_parameters():
                     # we skip lambda parameters as these require special initial values
                     # determined by config.layer_scale_init_value
                     if "lambda" in name:
                           continue
                     if param.requires_grad:
                           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'''   ,       )

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	Tuple   ,       snake_case__:	str   ,       snake_case__:	Any   ,       snake_case__:	List[Any]=2E-4   ,       snake_case__:	Optional[int]="outputs"   ,       snake_case__:	List[Any]=None	)  ->		List[str]:


         """simple docstring"""



         super().check_pt_tf_outputs(snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		List[str]:


         """simple docstring"""



         _lowercase     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case__	)

   @slow
   def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Optional[int]:


         """simple docstring"""



         for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase     =	DataaVecVisionModel.from_pretrained(snake_case__	)
               self.assertIsNotNone(snake_case__	)






def        UpperCAmelCase      (   ):


      '''simple docstring'''



      _lowercase     =	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 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		str:


         """simple docstring"""



         return (
             BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k'	) if is_vision_available() else None
         )

   @slow
   def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		Optional[int]:


         """simple docstring"""



         _lowercase     =	DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k'	).to(
             snake_case__	)

         _lowercase     =	self.default_image_processor
         _lowercase     =	prepare_img()
         _lowercase     =	image_processor(images=snake_case__   ,       return_tensors='pt'	).to(snake_case__	)

         # forward pass
         with torch.no_grad():
               _lowercase     =	model(**snake_case__	)
         _lowercase     =	outputs.logits

         # verify the logits
         _lowercase     =	torch.Size((1, 1_000)	)
         self.assertEqual(logits.shape   ,       snake_case__	)

         _lowercase     =	torch.tensor([0.32_77, -0.13_95, 0.09_11]	).to(snake_case__	)

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

         _lowercase     =	[model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
         self.assertEqual(logits[0].topk(2	).indices.cpu().tolist()   ,       snake_case__	)








import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase										=  logging.get_logger(__name__)

lowerCamelCase										=  {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class 					snake_case_							(       lowercase_      ):







   """simple docstring"""



   __lowerCAmelCase      =						"""hubert"""

   def __init__(    self:	Any   ,       snake_case__:	str=32   ,       snake_case__:	int=768   ,       snake_case__:	Dict=12   ,       snake_case__:	Optional[int]=12   ,       snake_case__:	List[str]=3_072   ,       snake_case__:	Optional[Any]="gelu"   ,       snake_case__:	Optional[int]=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	Optional[int]=0.1   ,       snake_case__:	List[str]=True   ,       snake_case__:	Union[str, Any]=0.0   ,       snake_case__:	Union[str, Any]=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	List[Any]=0.02   ,       snake_case__:	Dict=1E-5   ,       snake_case__:	Any="group"   ,       snake_case__:	Union[str, Any]="gelu"   ,       snake_case__:	List[str]=(512, 512, 512, 512, 512, 512, 512)   ,       snake_case__:	Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,       snake_case__:	Any=(10, 3, 3, 3, 3, 2, 2)   ,       snake_case__:	int=False   ,       snake_case__:	Any=128   ,       snake_case__:	Dict=16   ,       snake_case__:	Optional[Any]=False   ,       snake_case__:	Tuple=True   ,       snake_case__:	int=0.05   ,       snake_case__:	List[Any]=10   ,       snake_case__:	Tuple=2   ,       snake_case__:	List[Any]=0.0   ,       snake_case__:	Optional[int]=10   ,       snake_case__:	Optional[int]=0   ,       snake_case__:	Union[str, Any]="sum"   ,       snake_case__:	Union[str, Any]=False   ,       snake_case__:	int=False   ,       snake_case__:	Any=256   ,       snake_case__:	Union[str, Any]=0   ,       snake_case__:	Optional[int]=1   ,       snake_case__:	List[str]=2   ,       **snake_case__:	Optional[int]   ,       )  ->		Any:


       """simple docstring"""



       super().__init__(**snake_case__   ,       pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__	)
       _lowercase     =	hidden_size
       _lowercase     =	feat_extract_norm
       _lowercase     =	feat_extract_activation
       _lowercase     =	list(snake_case__	)
       _lowercase     =	list(snake_case__	)
       _lowercase     =	list(snake_case__	)
       _lowercase     =	conv_bias
       _lowercase     =	num_conv_pos_embeddings
       _lowercase     =	num_conv_pos_embedding_groups
       _lowercase     =	len(self.conv_dim	)
       _lowercase     =	num_hidden_layers
       _lowercase     =	intermediate_size
       _lowercase     =	hidden_act
       _lowercase     =	num_attention_heads
       _lowercase     =	hidden_dropout
       _lowercase     =	attention_dropout
       _lowercase     =	activation_dropout
       _lowercase     =	feat_proj_layer_norm
       _lowercase     =	feat_proj_dropout
       _lowercase     =	final_dropout
       _lowercase     =	layerdrop
       _lowercase     =	layer_norm_eps
       _lowercase     =	initializer_range
       _lowercase     =	vocab_size
       _lowercase     =	do_stable_layer_norm
       _lowercase     =	use_weighted_layer_sum
       _lowercase     =	classifier_proj_size

       if (
           (len(self.conv_stride	) != self.num_feat_extract_layers)
           or (len(self.conv_kernel	) != self.num_feat_extract_layers)
           or (len(self.conv_dim	) != self.num_feat_extract_layers)
       ):
           raise ValueError(
               'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
               ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
               f''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
               f''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       _lowercase     =	apply_spec_augment
       _lowercase     =	mask_time_prob
       _lowercase     =	mask_time_length
       _lowercase     =	mask_time_min_masks
       _lowercase     =	mask_feature_prob
       _lowercase     =	mask_feature_length
       _lowercase     =	mask_feature_min_masks

       # ctc loss
       _lowercase     =	ctc_loss_reduction
       _lowercase     =	ctc_zero_infinity

   @property
   def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Optional[int]:


       """simple docstring"""



       return functools.reduce(operator.mul   ,       self.conv_stride   ,       1	)
