


"""simple docstring"""

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    lowerCamelCase__						:
      def __init__(		self	:	str      ,       A_	:	int      ,       A_	:	str=1_3      ,       A_	:	List[str]=3_2      ,       A_	:	int=2      ,       A_	:	Tuple=3      ,       A_	:	Optional[Any]=1_6      ,       A_	:	Dict=[1, 2, 1]      ,       A_	:	Optional[int]=[2, 2, 4]      ,       A_	:	str=2      ,       A_	:	Optional[Any]=2.0      ,       A_	:	Union[str, Any]=True      ,       A_	:	int=0.0      ,       A_	:	str=0.0      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	List[str]="gelu"      ,       A_	:	List[Any]=False      ,       A_	:	List[Any]=True      ,       A_	:	Union[str, Any]=0.02      ,       A_	:	Tuple=1e-5      ,       A_	:	Dict=True      ,       A_	:	Any=None      ,       A_	:	List[str]=True      ,       A_	:	Optional[int]=1_0      ,       A_	:	Optional[int]=8      ,       ):






       '''simple docstring'''


       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	image_size
       __lowercase									=	patch_size
       __lowercase									=	num_channels
       __lowercase									=	embed_dim
       __lowercase									=	depths
       __lowercase									=	num_heads
       __lowercase									=	window_size
       __lowercase									=	mlp_ratio
       __lowercase									=	qkv_bias
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	drop_path_rate
       __lowercase									=	hidden_act
       __lowercase									=	use_absolute_embeddings
       __lowercase									=	patch_norm
       __lowercase									=	layer_norm_eps
       __lowercase									=	initializer_range
       __lowercase									=	is_training
       __lowercase									=	scope
       __lowercase									=	use_labels
       __lowercase									=	type_sequence_label_size
       __lowercase									=	encoder_stride
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


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

       __lowercase									=	None
       if self.use_labels:
        __lowercase									=	ids_tensor([self.batch_size]      ,       self.type_sequence_label_size							)

       __lowercase									=	self.get_config()

       return config, pixel_values, labels
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       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	:	List[Any]      ,       A_	:	str      ,       A_	:	Union[str, Any]      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	SwinvaModel(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_							)

       __lowercase									=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
       __lowercase									=	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	:	str      ,       A_	:	Tuple      ,       A_	:	Optional[Any]      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	SwinvaForMaskedImageModeling(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_							)
       self.parent.assertEqual(
           result.logits.shape      ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

       # test greyscale images
       __lowercase									=	1
       __lowercase									=	SwinvaForMaskedImageModeling(A_							)
       model.to(A_							)
       model.eval()

       __lowercase									=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       __lowercase									=	model(A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, 1, self.image_size, self.image_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	List[str]      ,       A_	:	Union[str, Any]      ,       A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.type_sequence_label_size
       __lowercase									=	SwinvaForImageClassification(A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_      ,       labels=A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


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






@require_torch
class    lowerCamelCase__						(		_a			,   _a			,   unittest.TestCase    ):
      a :     Tuple											=		(
          (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
      )
      a :     int											=		(
          {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
          if is_torch_available()
          else {}
      )

      a :     Dict											=		False
      a :     int											=		False
      a :     str											=		False
      a :     Dict											=		False
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	SwinvaModelTester(self							)
       __lowercase									=	ConfigTester(self      ,       config_class=A_      ,       embed_dim=3_7							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       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	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_							)
      @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       pass
      @unittest.skip(reason="""Swinv2 does not use inputs_embeds"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase									=	model_class(A_							)
        self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)							)
        __lowercase									=	model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(A_      ,       nn.Linear							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase									=	model_class(A_							)
        __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]      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


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

       for model_class in self.all_model_classes:
        __lowercase									=	True
        __lowercase									=	False
        __lowercase									=	True
        __lowercase									=	model_class(A_							)
        model.to(A_							)
        model.eval()
        with torch.no_grad():
         __lowercase									=	model(**self._prepare_for_class(A_      ,       A_							)							)
        __lowercase									=	outputs.attentions
        __lowercase									=	len(self.model_tester.depths							)
        self.assertEqual(len(A_							)      ,       A_							)

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        __lowercase									=	True
        __lowercase									=	config.window_size**2
        __lowercase									=	model_class(A_							)
        model.to(A_							)
        model.eval()
        with torch.no_grad():
         __lowercase									=	model(**self._prepare_for_class(A_      ,       A_							)							)
        __lowercase									=	outputs.attentions
        self.assertEqual(len(A_							)      ,       A_							)

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

        # Check attention is always last and order is fine
        __lowercase									=	True
        __lowercase									=	True
        __lowercase									=	model_class(A_							)
        model.to(A_							)
        model.eval()
        with torch.no_grad():
         __lowercase									=	model(**self._prepare_for_class(A_      ,       A_							)							)

        if hasattr(self.model_tester      ,       """num_hidden_states_types"""							):
         __lowercase									=	self.model_tester.num_hidden_states_types
        else:
         # also another +1 for reshaped_hidden_states
         __lowercase									=	2
        self.assertEqual(out_len + added_hidden_states      ,       len(A_							)							)

        __lowercase									=	outputs.attentions

        self.assertEqual(len(A_							)      ,       A_							)

        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	:	List[Any]      ,       A_	:	List[str]      ,       A_	:	int      ,       A_	:	Any      ,       A_	:	Dict							):






       '''simple docstring'''


       __lowercase									=	model_class(A_							)
       model.to(A_							)
       model.eval()

       with torch.no_grad():
        __lowercase									=	model(**self._prepare_for_class(A_      ,       A_							)							)

       __lowercase									=	outputs.hidden_states

       __lowercase									=	getattr(
           self.model_tester      ,       """expected_num_hidden_layers"""      ,       len(self.model_tester.depths							) + 1							)
       self.assertEqual(len(A_							)      ,       A_							)

       # Swinv2 has a different seq_length
       __lowercase									=	(
           config.patch_size
           if isinstance(config.patch_size      ,       collections.abc.Iterable							)
           else (config.patch_size, config.patch_size)
       )

       __lowercase									=	(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]      ,       )

       __lowercase									=	outputs.reshaped_hidden_states
       self.assertEqual(len(A_							)      ,       A_							)

       __lowercase							,		__lowercase							,		__lowercase							,		__lowercase									=	reshaped_hidden_states[0].shape
       __lowercase									=	(
           reshaped_hidden_states[0].view(A_      ,       A_      ,       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	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       __lowercase									=	(
           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:
        __lowercase									=	True
        self.check_hidden_states_output(A_      ,       A_      ,       A_      ,       A_							)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowercase									=	True

        self.check_hidden_states_output(A_      ,       A_      ,       A_      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()
       __lowercase									=	3

       __lowercase									=	(
           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)
       )
       __lowercase									=	(
           config.patch_size
           if isinstance(config.patch_size      ,       collections.abc.Iterable							)
           else (config.patch_size, config.patch_size)
       )

       __lowercase									=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       __lowercase									=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
        __lowercase									=	True
        self.check_hidden_states_output(A_      ,       A_      ,       A_      ,       (padded_height, padded_width)							)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowercase									=	True
        self.check_hidden_states_output(A_      ,       A_      ,       A_      ,       (padded_height, padded_width)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_							)
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowercase									=	SwinvaModel.from_pretrained(A_							)
        self.assertIsNotNone(A_							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       __lowercase									=	_config_zero_init(A_							)
       for model_class in self.all_model_classes:
        __lowercase									=	model_class(config=A_							)
        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    lowerCamelCase__						(		unittest.TestCase    ):
      @cached_property
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       return (
           AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""							)
           if is_vision_available()
           else None
       )






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""							).to(
           A_							)
       __lowercase									=	self.default_image_processor

       __lowercase									=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
       __lowercase									=	image_processor(images=A_      ,       return_tensors="""pt"""							).to(A_							)

       # forward pass
       with torch.no_grad():
        __lowercase									=	model(**A_							)

       # verify the logits
       __lowercase									=	torch.Size((1, 1_0_0_0)							)
       self.assertEqual(outputs.logits.shape      ,       A_							)
       __lowercase									=	torch.tensor([-0.39_47, -0.43_06, 0.00_26]							).to(A_							)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,       A_      ,       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
)
UpperCAmelCase__  							=logging.getLogger(__name__)

if __name__ == "__main__":
       UpperCAmelCase__  							=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=3_0522, type=int)
       UpperCAmelCase__  							=parser.parse_args()

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

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