
'''simple docstring'''



import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__					:						Tuple											=     get_tests_dir("fixtures/spiece.model")

@require_sentencepiece
@require_tokenizers
class       _a (_lowerCamelCase     ,    unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     ErnieMTokenizer
       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     True
       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     False






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          super().setUp()

          # We have a SentencePiece fixture for testing
          _SCREAMING_SNAKE_CASE          = ErnieMTokenizer(A__				,							unk_token="""<unk>"""				,							pad_token="""<pad>"""					)
          tokenizer.save_pretrained(self.tmpdirname					)






       def 				UpperCamelCase (							self				,							A__					)			->	str:
          _SCREAMING_SNAKE_CASE          = """this is a test"""
          _SCREAMING_SNAKE_CASE          = """this is a test"""
          return input_text, output_text






       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE          = """<pad>"""
          _SCREAMING_SNAKE_CASE          = 0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(A__					)				,							A__					)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(A__					)				,							A__					)






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          _SCREAMING_SNAKE_CASE          = list(self.get_tokenizer().get_vocab().keys()					)

          self.assertEqual(vocab_keys[0]				,							"""<pad>"""					)
          self.assertEqual(vocab_keys[1]				,							"""<unk>"""					)
          self.assertEqual(vocab_keys[-1]				,							"""▁eloquent"""					)
          self.assertEqual(len(A__					)				,							3_00_00					)






       def 				UpperCamelCase (							self					)			->	List[str]:
          self.assertEqual(self.get_tokenizer().vocab_size				,							3_00_00					)






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          if not self.test_rust_tokenizer:
             return

          _SCREAMING_SNAKE_CASE          = self.get_tokenizer()
          _SCREAMING_SNAKE_CASE          = self.get_rust_tokenizer()

          _SCREAMING_SNAKE_CASE          = """I was born in 92000, and this is falsé."""

          _SCREAMING_SNAKE_CASE          = tokenizer.tokenize(A__					)
          _SCREAMING_SNAKE_CASE          = rust_tokenizer.tokenize(A__					)
          self.assertListEqual(A__				,							A__					)

          _SCREAMING_SNAKE_CASE          = tokenizer.encode(A__				,							add_special_tokens=A__					)
          _SCREAMING_SNAKE_CASE          = rust_tokenizer.encode(A__				,							add_special_tokens=A__					)
          self.assertListEqual(A__				,							A__					)

          _SCREAMING_SNAKE_CASE          = self.get_rust_tokenizer()
          _SCREAMING_SNAKE_CASE          = tokenizer.encode(A__					)
          _SCREAMING_SNAKE_CASE          = rust_tokenizer.encode(A__					)
          self.assertListEqual(A__				,							A__					)






       def 				UpperCamelCase (							self					)			->	List[str]:
          _SCREAMING_SNAKE_CASE          = ErnieMTokenizer(A__				,							do_lower_case=A__				,							unk_token="""<unk>"""				,							pad_token="""<pad>"""					)

          _SCREAMING_SNAKE_CASE          = tokenizer.tokenize("""This is a test"""					)
          self.assertListEqual(A__				,							["""▁this""", """▁is""", """▁a""", """▁test"""]					)

          self.assertListEqual(tokenizer.convert_tokens_to_ids(A__					)				,							[48, 25, 21, 12_89]					)

          _SCREAMING_SNAKE_CASE          = tokenizer.tokenize("""I was born in 92000, and this is falsé."""					)
          # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
          self.assertListEqual(
              A__				,							["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]					)
          _SCREAMING_SNAKE_CASE          = tokenizer.convert_tokens_to_ids(A__					)
          self.assertListEqual(A__				,							[31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]					)

          _SCREAMING_SNAKE_CASE          = tokenizer.convert_ids_to_tokens(A__					)
          self.assertListEqual(
              A__				,							["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]				,							)






       def 				UpperCamelCase (							self					)			->	Dict:
          _SCREAMING_SNAKE_CASE          = ErnieMTokenizer(A__				,							unk_token="""<unk>"""				,							pad_token="""<pad>"""					)

          _SCREAMING_SNAKE_CASE          = tokenizer.encode("""sequence builders"""					)
          _SCREAMING_SNAKE_CASE          = tokenizer.encode("""multi-sequence build"""					)

          _SCREAMING_SNAKE_CASE          = tokenizer.build_inputs_with_special_tokens(A__					)
          _SCREAMING_SNAKE_CASE          = tokenizer.build_inputs_with_special_tokens(A__				,							A__					)

          assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
          assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
              tokenizer.sep_token_id
          ] + text_a + [tokenizer.sep_token_id]






       @slow
       def 				UpperCamelCase (							self					)			->	List[Any]:
          # fmt: off
          _SCREAMING_SNAKE_CASE          = {"""input_ids""": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=A__				,							model_name="""susnato/ernie-m-base_pytorch"""				,							sequences=[
                  """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
                  """general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
                  """Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained """
                  """models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
                  """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
                  """conditioning on both left and right context in all layers.""",
                  """The quick brown fox jumps over the lazy dog.""",
              ]				,							)








'''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       _a :





       """simple docstring"""




       def __init__(							self				,							A__				,							A__=13				,							A__=32				,							A__=1				,							A__=3				,							A__=16				,							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				,							)			->	Optional[Any]:
          _SCREAMING_SNAKE_CASE          = parent
          _SCREAMING_SNAKE_CASE          = batch_size
          _SCREAMING_SNAKE_CASE          = image_size
          _SCREAMING_SNAKE_CASE          = patch_size
          _SCREAMING_SNAKE_CASE          = num_channels
          _SCREAMING_SNAKE_CASE          = embed_dim
          _SCREAMING_SNAKE_CASE          = depths
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = window_size
          _SCREAMING_SNAKE_CASE          = mlp_ratio
          _SCREAMING_SNAKE_CASE          = qkv_bias
          _SCREAMING_SNAKE_CASE          = hidden_dropout_prob
          _SCREAMING_SNAKE_CASE          = attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE          = drop_path_rate
          _SCREAMING_SNAKE_CASE          = hidden_act
          _SCREAMING_SNAKE_CASE          = use_absolute_embeddings
          _SCREAMING_SNAKE_CASE          = patch_norm
          _SCREAMING_SNAKE_CASE          = layer_norm_eps
          _SCREAMING_SNAKE_CASE          = initializer_range
          _SCREAMING_SNAKE_CASE          = is_training
          _SCREAMING_SNAKE_CASE          = scope
          _SCREAMING_SNAKE_CASE          = use_labels
          _SCREAMING_SNAKE_CASE          = upscale

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






       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE          = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

          _SCREAMING_SNAKE_CASE          = self.get_config()

          return config, pixel_values, labels






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          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 				UpperCamelCase (							self				,							A__				,							A__				,							A__					)			->	List[str]:
          _SCREAMING_SNAKE_CASE          = SwinaSRModel(config=A__					)
          model.to(A__					)
          model.eval()
          _SCREAMING_SNAKE_CASE          = model(A__					)

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






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__					)			->	Dict:
          _SCREAMING_SNAKE_CASE          = SwinaSRForImageSuperResolution(A__					)
          model.to(A__					)
          model.eval()
          _SCREAMING_SNAKE_CASE          = model(A__					)

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






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          _SCREAMING_SNAKE_CASE          = self.prepare_config_and_inputs()
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = config_and_inputs
          _SCREAMING_SNAKE_CASE          = {"""pixel_values""": pixel_values}
          return config, inputs_dict






@require_torch
class       _a (_lowerCamelCase     ,    _lowerCamelCase     ,    unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE					=     {'feature-extraction': SwinaSRModel} if is_torch_available() else {}

       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     False






       def 				UpperCamelCase (							self					)			->	List[str]:
          _SCREAMING_SNAKE_CASE          = SwinaSRModelTester(self					)
          _SCREAMING_SNAKE_CASE          = ConfigTester(self				,							config_class=A__				,							embed_dim=37					)






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          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 				UpperCamelCase (							self					)			->	int:
          _SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__					)






       def 				UpperCamelCase (							self					)			->	List[Any]:
          _SCREAMING_SNAKE_CASE          = 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 				UpperCamelCase (							self					)			->	List[Any]:
          pass






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






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






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






       def 				UpperCamelCase (							self					)			->	Dict:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs_for_common()

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






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs_for_common()

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

             _SCREAMING_SNAKE_CASE          = ["""pixel_values"""]
             self.assertListEqual(arg_names[:1]				,							A__					)






       @slow
       def 				UpperCamelCase (							self					)			->	Optional[int]:
          for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _SCREAMING_SNAKE_CASE          = SwinaSRModel.from_pretrained(A__					)
             self.assertIsNotNone(A__					)






       def 				UpperCamelCase (							self					)			->	List[str]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = _config_zero_init(A__					)
          for model_class in self.all_model_classes:
             _SCREAMING_SNAKE_CASE          = 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 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE          = True

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

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

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

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

             _SCREAMING_SNAKE_CASE          = 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       _a (unittest.TestCase):





       """simple docstring"""




       def 				UpperCamelCase (							self					)			->	Dict:
          _SCREAMING_SNAKE_CASE          = SwinaSRImageProcessor()
          _SCREAMING_SNAKE_CASE          = SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64"""					).to(A__					)

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

          # forward pass
          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					)

          # verify the logits
          _SCREAMING_SNAKE_CASE          = torch.Size([1, 3, 9_76, 12_96]					)
          self.assertEqual(outputs.reconstruction.shape				,							A__					)
          _SCREAMING_SNAKE_CASE          = torch.tensor(
              [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]					).to(A__					)
          self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]				,							A__				,							atol=1E-4					)					)






