


import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
     from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase=13					,  _lowerCamelCase=32					,  _lowerCamelCase=2					,  _lowerCamelCase=3					,  _lowerCamelCase=16					,  _lowerCamelCase=[1, 2, 1]					,  _lowerCamelCase=[2, 2, 4]					,  _lowerCamelCase=2					,  _lowerCamelCase=2.0					,  _lowerCamelCase=True					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.1					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=False					,  _lowerCamelCase=True					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1e-5					,  _lowerCamelCase=True					,  _lowerCamelCase=None					,  _lowerCamelCase=True					,  _lowerCamelCase=10					,  _lowerCamelCase=8					,  ):
       a						:Optional[Any] 							=       parent
       a						:Dict 							=       batch_size
       a						:List[Any] 							=       image_size
       a						:Dict 							=       patch_size
       a						:int 							=       num_channels
       a						:Dict 							=       embed_dim
       a						:Any 							=       depths
       a						:Union[str, Any] 							=       num_heads
       a						:List[str] 							=       window_size
       a						:List[str] 							=       mlp_ratio
       a						:Dict 							=       qkv_bias
       a						:Dict 							=       hidden_dropout_prob
       a						:Dict 							=       attention_probs_dropout_prob
       a						:Union[str, Any] 							=       drop_path_rate
       a						:int 							=       hidden_act
       a						:Any 							=       use_absolute_embeddings
       a						:List[str] 							=       patch_norm
       a						:Optional[Any] 							=       layer_norm_eps
       a						:str 							=       initializer_range
       a						:Union[str, Any] 							=       is_training
       a						:Optional[int] 							=       scope
       a						:Tuple 							=       use_labels
       a						:Tuple 							=       type_sequence_label_size
       a						:List[Any] 							=       encoder_stride




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       a						:Dict 							=       None
       if self.use_labels:
             a						:Optional[Any] 							=       ids_tensor([self.batch_size]					,  self.type_sequence_label_size    )

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

       return config, pixel_values, labels




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return SwinvaConfig(
           image_size=self.image_size					,  patch_size=self.patch_size					,  num_channels=self.num_channels					,  embed_dim=self.embed_dim					,  depths=self.depths					,  num_heads=self.num_heads					,  window_size=self.window_size					,  mlp_ratio=self.mlp_ratio					,  qkv_bias=self.qkv_bias					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  drop_path_rate=self.drop_path_rate					,  hidden_act=self.hidden_act					,  use_absolute_embeddings=self.use_absolute_embeddings					,  path_norm=self.patch_norm					,  layer_norm_eps=self.layer_norm_eps					,  initializer_range=self.initializer_range					,  encoder_stride=self.encoder_stride					,  )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:List[str] 							=       SwinvaModel(config=_lowerCamelCase    )
       model.to(_lowerCamelCase    )
       model.eval()
       a						:str 							=       model(_lowerCamelCase    )

       a						:Any 							=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
       a						:List[Any] 							=       int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

       self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, expected_seq_len, expected_dim)    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:List[Any] 							=       SwinvaForMaskedImageModeling(config=_lowerCamelCase    )
       model.to(_lowerCamelCase    )
       model.eval()
       a						:Union[str, Any] 							=       model(_lowerCamelCase    )
       self.parent.assertEqual(
           result.logits.shape					,  (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

       # test greyscale images
       a						:Any 							=       1
       a						:Tuple 							=       SwinvaForMaskedImageModeling(_lowerCamelCase    )
       model.to(_lowerCamelCase    )
       model.eval()

       a						:List[Any] 							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
       a						:Union[str, Any] 							=       model(_lowerCamelCase    )
       self.parent.assertEqual(result.logits.shape					,  (self.batch_size, 1, self.image_size, self.image_size)    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:List[Any] 							=       self.type_sequence_label_size
       a						:int 							=       SwinvaForImageClassification(_lowerCamelCase    )
       model.to(_lowerCamelCase    )
       model.eval()
       a						:Tuple 							=       model(_lowerCamelCase					,  labels=_lowerCamelCase    )
       self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size)    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       self.prepare_config_and_inputs()
       a							,				a							,				a						:int 							=       config_and_inputs
       a						:Any 							=       {'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class 		_snake_case  (      snake_case_						,    snake_case_						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       (
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 SCREAMING_SNAKE_CASE__      =       (
     {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 SCREAMING_SNAKE_CASE__      =       False
 SCREAMING_SNAKE_CASE__      =       False
 SCREAMING_SNAKE_CASE__      =       False
 SCREAMING_SNAKE_CASE__      =       False




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       SwinvaModelTester(self    )
       a						:str 							=       ConfigTester(self					,  config_class=_lowerCamelCase					,  embed_dim=37    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase    )




 @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''    )
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''    )
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a							,				a						:Union[str, Any] 							=       self.model_tester.prepare_config_and_inputs_for_common()

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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a							,				a						:Dict 							=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             a						:List[Any] 							=       model_class(_lowerCamelCase    )
             a						:Dict 							=       inspect.signature(model.forward    )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             a						:Optional[Any] 							=       [*signature.parameters.keys()]

             a						:int 							=       ['''pixel_values''']
             self.assertListEqual(arg_names[:1]					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a							,				a						:Optional[Any] 							=       self.model_tester.prepare_config_and_inputs_for_common()
       a						:List[Any] 							=       True

       for model_class in self.all_model_classes:
             a						:List[Any] 							=       True
             a						:Any 							=       False
             a						:Optional[int] 							=       True
             a						:int 							=       model_class(_lowerCamelCase    )
             model.to(_lowerCamelCase    )
             model.eval()
             with torch.no_grad():
                   a						:List[str] 							=       model(**self._prepare_for_class(_lowerCamelCase					,  _lowerCamelCase    )    )
             a						:int 							=       outputs.attentions
             a						:Union[str, Any] 							=       len(self.model_tester.depths    )
             self.assertEqual(len(_lowerCamelCase    )					,  _lowerCamelCase    )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             a						:Tuple 							=       True
             a						:Optional[Any] 							=       config.window_size**2
             a						:List[str] 							=       model_class(_lowerCamelCase    )
             model.to(_lowerCamelCase    )
             model.eval()
             with torch.no_grad():
                   a						:Tuple 							=       model(**self._prepare_for_class(_lowerCamelCase					,  _lowerCamelCase    )    )
             a						:Optional[int] 							=       outputs.attentions
             self.assertEqual(len(_lowerCamelCase    )					,  _lowerCamelCase    )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]    )					,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,  )
             a						:Tuple 							=       len(_lowerCamelCase    )

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

             if hasattr(self.model_tester					,  '''num_hidden_states_types'''    ):
                   a						:Optional[Any] 							=       self.model_tester.num_hidden_states_types
             else:
                   # also another +1 for reshaped_hidden_states
                   a						:Union[str, Any] 							=       2
             self.assertEqual(out_len + added_hidden_states					,  len(_lowerCamelCase    )    )

             a						:Tuple 							=       outputs.attentions

             self.assertEqual(len(_lowerCamelCase    )					,  _lowerCamelCase    )

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]    )					,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,  )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:str 							=       model_class(_lowerCamelCase    )
       model.to(_lowerCamelCase    )
       model.eval()

       with torch.no_grad():
             a						:str 							=       model(**self._prepare_for_class(_lowerCamelCase					,  _lowerCamelCase    )    )

       a						:Dict 							=       outputs.hidden_states

       a						:int 							=       getattr(
           self.model_tester					,  '''expected_num_hidden_layers'''					,  len(self.model_tester.depths    ) + 1    )
       self.assertEqual(len(_lowerCamelCase    )					,  _lowerCamelCase    )

       # Swinv2 has a different seq_length
       a						:List[Any] 							=       (
           config.patch_size
           if isinstance(config.patch_size					,  collections.abc.Iterable    )
           else (config.patch_size, config.patch_size)
       )

       a						:int 							=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

       a						:List[Any] 							=       outputs.reshaped_hidden_states
       self.assertEqual(len(_lowerCamelCase    )					,  _lowerCamelCase    )

       a							,				a							,				a							,				a						:Any 							=       reshaped_hidden_states[0].shape
       a						:Any 							=       (
           reshaped_hidden_states[0].view(_lowerCamelCase					,  _lowerCamelCase					,  height * width    ).permute(0					,  2					,  1    )
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]    )					,  [num_patches, self.model_tester.embed_dim]					,  )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a							,				a						:Optional[int] 							=       self.model_tester.prepare_config_and_inputs_for_common()

       a						:Optional[Any] 							=       (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size					,  collections.abc.Iterable    )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
             a						:str 							=       True
             self.check_hidden_states_output(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             a						:List[str] 							=       True

             self.check_hidden_states_output(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a							,				a						:Optional[Any] 							=       self.model_tester.prepare_config_and_inputs_for_common()
       a						:Dict 							=       3

       a						:str 							=       (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size					,  collections.abc.Iterable    )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       a						:int 							=       (
           config.patch_size
           if isinstance(config.patch_size					,  collections.abc.Iterable    )
           else (config.patch_size, config.patch_size)
       )

       a						:Dict 							=       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       a						:str 							=       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             a						:Dict 							=       True
             self.check_hidden_states_output(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  (padded_height, padded_width)    )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             a						:Union[str, Any] 							=       True
             self.check_hidden_states_output(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  (padded_height, padded_width)    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase    )




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a						:Any 							=       SwinvaModel.from_pretrained(_lowerCamelCase    )
             self.assertIsNotNone(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a							,				a						:List[Any] 							=       self.model_tester.prepare_config_and_inputs_for_common()

       a						:Union[str, Any] 							=       _config_zero_init(_lowerCamelCase    )
       for model_class in self.all_model_classes:
             a						:List[Any] 							=       model_class(config=_lowerCamelCase    )
             for name, param in model.named_parameters():
                   if "embeddings" not in name and "logit_scale" not in name and 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'''					,  )







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




 @cached_property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return (
           AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''    )
           if is_vision_available()
           else None
       )




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''    ).to(
           _lowerCamelCase    )
       a						:Optional[int] 							=       self.default_image_processor

       a						:Dict 							=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
       a						:List[Any] 							=       image_processor(images=_lowerCamelCase					,  return_tensors='''pt'''    ).to(_lowerCamelCase    )

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

       # verify the logits
       a						:List[Any] 							=       torch.Size((1, 1000)    )
       self.assertEqual(outputs.logits.shape					,  _lowerCamelCase    )
       a						:List[Any] 							=       torch.tensor([-0.3947, -0.4306, 0.0026]    ).to(_lowerCamelCase    )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]					,  _lowerCamelCase					,  atol=1e-4    )    )












'''simple docstring'''

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_					= logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=30_522, type=int)
      lowercase_					= parser.parse_args()

      logger.info(f"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase_					= pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase_					= Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase_					= [0] * args.vocab_size
      for k, v in counter.items():
            lowercase_					= v

      logger.info(f"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



