



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:     int  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Optional[int]=100  ,lowerCamelCase_:     Any=13  ,lowerCamelCase_:     str=30  ,lowerCamelCase_:     Any=2  ,lowerCamelCase_:     Tuple=3  ,lowerCamelCase_:     Tuple=True  ,lowerCamelCase_:     Tuple=True  ,lowerCamelCase_:     int=32  ,lowerCamelCase_:     Dict=4  ,lowerCamelCase_:     Optional[int]=4  ,lowerCamelCase_:     List[str]=37  ,lowerCamelCase_:     str="gelu"  ,lowerCamelCase_:     Any=0.1  ,lowerCamelCase_:     Optional[Any]=0.1  ,lowerCamelCase_:     List[str]=10  ,lowerCamelCase_:     Tuple=0.0_2  ,lowerCamelCase_:     List[str]=3  ,lowerCamelCase_:     Dict=None  ,lowerCamelCase_:     Optional[Any]=[0, 1, 2, 3]  ,)	->      Union[str, Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					parent
       UpperCAmelCase_     :       str  		=					100
       UpperCAmelCase_     :       Dict  		=					batch_size
       UpperCAmelCase_     :       Union[str, Any]  		=					image_size
       UpperCAmelCase_     :       str  		=					patch_size
       UpperCAmelCase_     :       List[str]  		=					num_channels
       UpperCAmelCase_     :       Union[str, Any]  		=					is_training
       UpperCAmelCase_     :       Tuple  		=					use_labels
       UpperCAmelCase_     :       List[Any]  		=					hidden_size
       UpperCAmelCase_     :       Optional[int]  		=					num_hidden_layers
       UpperCAmelCase_     :       Tuple  		=					num_attention_heads
       UpperCAmelCase_     :       Optional[Any]  		=					intermediate_size
       UpperCAmelCase_     :       Union[str, Any]  		=					hidden_act
       UpperCAmelCase_     :       int  		=					hidden_dropout_prob
       UpperCAmelCase_     :       Any  		=					attention_probs_dropout_prob
       UpperCAmelCase_     :       List[str]  		=					type_sequence_label_size
       UpperCAmelCase_     :       Tuple  		=					initializer_range
       UpperCAmelCase_     :       Optional[int]  		=					scope
       UpperCAmelCase_     :       List[str]  		=					out_indices
       UpperCAmelCase_     :       List[str]  		=					num_labels

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


   def       A__    (    self:     List[str]							)	->      Optional[int]:
       UpperCAmelCase_     :       List[str]  		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCAmelCase_     :       Tuple  		=					None
       UpperCAmelCase_     :       Dict  		=					None
       if self.use_labels:
           UpperCAmelCase_     :       Tuple  		=					ids_tensor([self.batch_size]  ,self.type_sequence_label_size							)
           UpperCAmelCase_     :       Any  		=					ids_tensor([self.batch_size, self.image_size, self.image_size]  ,self.num_labels							)

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

       return config, pixel_values, labels, pixel_labels


   def       A__    (    self:     Any							)	->      str:
       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       A__    (    self:     Tuple  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[int]							)	->      List[Any]:
       UpperCAmelCase_     :       Optional[Any]  		=					DataaVecVisionModel(config=__snake_case							)
       model.to(__snake_case							)
       model.eval()
       UpperCAmelCase_     :       Any  		=					model(__snake_case							)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       UpperCAmelCase_     :       int  		=					(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       A__    (    self:     Any  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Any							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Optional[int]  		=					self.type_sequence_label_size
       UpperCAmelCase_     :       Tuple  		=					DataaVecVisionForImageClassification(__snake_case							)
       model.to(__snake_case							)
       model.eval()
       UpperCAmelCase_     :       int  		=					model(__snake_case  ,labels=__snake_case							)
       self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)							)


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Dict							)	->      Optional[Any]:
       UpperCAmelCase_     :       List[Any]  		=					self.num_labels
       UpperCAmelCase_     :       Dict  		=					DataaVecVisionForSemanticSegmentation(__snake_case							)
       model.to(__snake_case							)
       model.eval()
       UpperCAmelCase_     :       str  		=					model(__snake_case							)
       self.parent.assertEqual(
           result.logits.shape  ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)
       UpperCAmelCase_     :       Any  		=					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       A__    (    self:     Tuple							)	->      str:
       UpperCAmelCase_     :       Optional[Any]  		=					self.prepare_config_and_inputs()
       UpperCAmelCase_     :       Dict  		=					config_and_inputs
       UpperCAmelCase_     :       Optional[Any]  		=					{'''pixel_values''': pixel_values}
       return config, inputs_dict







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






   '''simple docstring'''






   A__      : str       =						(
       (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   A__      : int       =						(
       {
           'feature-extraction': DataaVecVisionModel,
           'image-classification': DataaVecVisionForImageClassification,
           'image-segmentation': DataaVecVisionForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   A__      : Tuple       =						False
   A__      : int       =						False
   A__      : List[str]       =						False


   def       A__    (    self:     Union[str, Any]							)	->      Optional[Any]:
       UpperCAmelCase_     :       List[Any]  		=					DataaVecVisionModelTester(self							)
       UpperCAmelCase_     :       Dict  		=					ConfigTester(
           self  ,config_class=__snake_case  ,has_text_modality=__snake_case  ,hidden_size=37							)


   def       A__    (    self:     Union[str, Any]							)	->      Any:
       self.config_tester.run_common_tests()


   def       A__    (    self:     Optional[Any]							)	->      List[str]:
       pass


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


   def       A__    (    self:     Any							)	->      int:
       UpperCAmelCase_     :       int  		=					self.model_tester.prepare_config_and_inputs_for_common()

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


   def       A__    (    self:     Union[str, Any]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Dict  		=					self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCAmelCase_     :       List[Any]  		=					['''pixel_values''']
           self.assertListEqual(arg_names[:1]  ,__snake_case							)


   def       A__    (    self:     Optional[Any]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Dict  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__snake_case							)


   def       A__    (    self:     Union[str, Any]							)	->      int:
       UpperCAmelCase_     :       Dict  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_segmentation(*__snake_case							)


   def       A__    (    self:     Union[str, Any]							)	->      str:
       if not self.model_tester.is_training:
           return

       UpperCAmelCase_     :       Tuple  		=					self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_     :       List[str]  		=					True

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

           UpperCAmelCase_     :       Union[str, Any]  		=					model_class(__snake_case							)
           model.to(__snake_case							)
           model.train()
           UpperCAmelCase_     :       List[Any]  		=					self._prepare_for_class(__snake_case  ,__snake_case  ,return_labels=__snake_case							)
           UpperCAmelCase_     :       Any  		=					model(**__snake_case							).loss
           loss.backward()


   def       A__    (    self:     int							)	->      Dict:
       UpperCAmelCase_     :       Dict  		=					self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
           return

       UpperCAmelCase_     :       str  		=					False
       UpperCAmelCase_     :       Union[str, Any]  		=					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":
               UpperCAmelCase_     :       Any  		=					inputs_dict['''pixel_values'''].shape
               UpperCAmelCase_     :       Optional[int]  		=					torch.zeros(
                   [self.model_tester.batch_size, height, width]  ,device=__snake_case							).long()
           UpperCAmelCase_     :       Union[str, Any]  		=					model_class(__snake_case							)
           model.gradient_checkpointing_enable()
           model.to(__snake_case							)
           model.train()
           UpperCAmelCase_     :       Union[str, Any]  		=					self._prepare_for_class(__snake_case  ,__snake_case  ,return_labels=__snake_case							)
           UpperCAmelCase_     :       List[str]  		=					model(**__snake_case							).loss
           loss.backward()


   def       A__    (    self:     List[Any]							)	->      int:
       UpperCAmelCase_     :       List[str]  		=					self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase_     :       List[Any]  		=					_config_zero_init(__snake_case							)
       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Optional[int]  		=					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       A__    (    self:     Any  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     int=2e-4  ,lowerCamelCase_:     Optional[Any]="outputs"  ,lowerCamelCase_:     Tuple=None							)	->      Optional[int]:
       super().check_pt_tf_outputs(__snake_case  ,__snake_case  ,__snake_case  ,__snake_case  ,__snake_case  ,__snake_case							)


   def       A__    (    self:     Tuple							)	->      Tuple:
       UpperCAmelCase_     :       List[str]  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__snake_case							)






   @slow
   def       A__    (    self:     Optional[Any]							)	->      int:
       for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCAmelCase_     :       Optional[int]  		=					DataaVecVisionModel.from_pretrained(__snake_case							)
           self.assertIsNotNone(__snake_case							)







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Tuple  		=					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       A__    (    self:     Optional[Any]							)	->      Tuple:
       return (
           BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""							) if is_vision_available() else None
       )






   @slow
   def       A__    (    self:     int							)	->      int:
       UpperCAmelCase_     :       int  		=					DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""							).to(
           __snake_case							)

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

       # forward pass
       with torch.no_grad():
           UpperCAmelCase_     :       int  		=					model(**__snake_case							)
       UpperCAmelCase_     :       Optional[int]  		=					outputs.logits

       # verify the logits
       UpperCAmelCase_     :       List[str]  		=					torch.Size((1, 1000)							)
       self.assertEqual(logits.shape  ,__snake_case							)

       UpperCAmelCase_     :       Optional[Any]  		=					torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1]							).to(__snake_case							)

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

       UpperCAmelCase_     :       List[Any]  		=					[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 argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def   snake_case_    (			__lowercase      ,							__lowercase							):
   # Initialize PyTorch model
   UpperCAmelCase_				:   Optional[Any]							= CanineConfig()
   UpperCAmelCase_				:   Any							= CanineModel(__lowercase							)
   model.eval()

   print(F'''Building PyTorch model from configuration: {config}'''							)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(__lowercase      ,							__lowercase      ,							__lowercase							)

   # Save pytorch-model (weights and configuration)
   print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
   model.save_pretrained(__lowercase							)

   # Save tokenizer files
   UpperCAmelCase_				:   List[Any]							= CanineTokenizer()
   print(F'''Save tokenizer files to {pytorch_dump_path}'''							)
   tokenizer.save_pretrained(__lowercase							)


if __name__ == "__main__":
       __UpperCamelCase					:	int          =				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path',
           default=None,
           type=str,
           required=True,
           help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
       )
       parser.add_argument(
           '--pytorch_dump_path',
           default=None,
           type=str,
           required=True,
           help='Path to a folder where the PyTorch model will be placed.',
       )
       __UpperCamelCase					:	str          =				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)