






"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode






def 							lowerCAmelCase			(   UpperCamelCase_:				List[str]=10       )							->				Union[str, Any]:







  '''simple docstring'''







  for _ in tqdm(range(0  ,			args.n_examples  ,			UpperCamelCase_       )       ):
    yield [next(UpperCamelCase_       )[args.text_column] for _ in range(UpperCamelCase_       )]


# Configuration
UpperCamelCase											=     HfArgumentParser(TokenizerTrainingArguments)
UpperCamelCase											=     parser.parse_args()

# Base tokenizer
UpperCamelCase											=     AutoTokenizer.from_pretrained(args.base_tokenizer)
UpperCamelCase											=     list(bytes_to_unicode().values())

# Load dataset
UpperCamelCase											=     load_dataset(args.dataset_name, split="""train""", streaming=True)
UpperCamelCase											=     iter(dataset)


# Training and saving
UpperCamelCase											=     tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)




new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)










"""simple docstring"""




import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import 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 SwinaSRForImageSuperResolution, SwinaSRModel
     from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import SwinaSRImageProcessor




class  lowercase_     :






      def __init__(     self  ,  a_  ,  a_=1_3  ,  a_=3_2  ,  a_=1  ,  a_=3  ,  a_=1_6  ,  a_=[1, 2, 1]  ,  a_=[2, 2, 4]  ,  a_=2  ,  a_=2.0  ,  a_=True  ,  a_=0.0  ,  a_=0.0  ,  a_=0.1  ,  a_="gelu"  ,  a_=False  ,  a_=True  ,  a_=0.02  ,  a_=1E-5  ,  a_=True  ,  a_=None  ,  a_=False  ,  a_=2  ,  )						->str:







        '''simple docstring'''


        _a					      =						parent
        _a					      =						batch_size
        _a					      =						image_size
        _a					      =						patch_size
        _a					      =						num_channels
        _a					      =						embed_dim
        _a					      =						depths
        _a					      =						num_heads
        _a					      =						window_size
        _a					      =						mlp_ratio
        _a					      =						qkv_bias
        _a					      =						hidden_dropout_prob
        _a					      =						attention_probs_dropout_prob
        _a					      =						drop_path_rate
        _a					      =						hidden_act
        _a					      =						use_absolute_embeddings
        _a					      =						patch_norm
        _a					      =						layer_norm_eps
        _a					      =						initializer_range
        _a					      =						is_training
        _a					      =						scope
        _a					      =						use_labels
        _a					      =						upscale

        # here we set some attributes to make tests pass
        _a					      =						len(a_						)
        _a					      =						embed_dim
        _a					      =						(image_size // patch_size) ** 2






      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


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

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

        _a					      =						self.get_config()

        return config, pixel_values, labels






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







        '''simple docstring'''


        return SwinaSRConfig(
            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  ,  upscale=self.upscale  ,  )






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







        '''simple docstring'''


        _a					      =						SwinaSRModel(config=a_						)
        model.to(a_						)
        model.eval()
        _a					      =						model(a_						)

        self.parent.assertEqual(
            result.last_hidden_state.shape  ,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)						)






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						SwinaSRForImageSuperResolution(a_						)
        model.to(a_						)
        model.eval()
        _a					      =						model(a_						)

        _a					      =						self.image_size * self.upscale
        self.parent.assertEqual(
            result.reconstruction.shape  ,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)						)






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


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







@require_torch
class  lowercase_     (_UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase  ):
      A__						:		str							      =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      A__						:		Optional[Any]							      =					{'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

      A__						:		List[str]							      =					False
      A__						:		Optional[Any]							      =					False
      A__						:		str							      =					False
      A__						:		Optional[int]							      =					False
      A__						:		List[Any]							      =					False






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







        '''simple docstring'''


        _a					      =						SwinaSRModelTester(self						)
        _a					      =						ConfigTester(self  ,  config_class=a_  ,  embed_dim=3_7						)






      def       lowerCamelCase__     (     self						)						->Union[str, 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       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        _a					      =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a_						)






      def       lowerCamelCase__     (     self						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_super_resolution(*a_						)






      @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."						)
      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not use inputs_embeds"						)
      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not support training yet"						)
      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not support training yet"						)
      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


        pass






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

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






      def       lowerCamelCase__     (     self						)						->Optional[Any]:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

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

          _a					      =						["pixel_values"]
          self.assertListEqual(arg_names[:1]  ,  a_						)






      @slow
      def       lowerCamelCase__     (     self						)						->Dict:







        '''simple docstring'''


        for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _a					      =						SwinaSRModel.from_pretrained(a_						)
          self.assertIsNotNone(a_						)






      def       lowerCamelCase__     (     self						)						->Dict:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

        _a					      =						_config_zero_init(a_						)
        for model_class in self.all_model_classes:
          _a					      =						model_class(config=a_						)
          for name, param in model.named_parameters():
            if "logit_scale" 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       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


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

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

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

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

          self.assertEqual(out_len + 1  ,  len(a_						)						)

          _a					      =						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]  ,  )







@require_vision
@require_torch
@slow
class  lowercase_     (unittest.TestCase  ):






      def       lowerCamelCase__     (     self						)						->Optional[Any]:







        '''simple docstring'''


        _a					      =						SwinaSRImageProcessor()
        _a					      =						SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"						).to(a_						)

        _a					      =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
        _a					      =						processor(images=a_  ,  return_tensors="pt"						).to(a_						)

        # forward pass
        with torch.no_grad():
          _a					      =						model(**a_						)

        # verify the logits
        _a					      =						torch.Size([1, 3, 9_7_6, 1_2_9_6]						)
        self.assertEqual(outputs.reconstruction.shape  ,  a_						)
        _a					      =						torch.tensor(
            [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]						).to(a_						)
        self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]  ,  a_  ,  atol=1E-4						)						)


