




'''simple docstring'''





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


a		:	List[Any]       =      5_0_0_0_0_0

a      ,				a		:	Optional[int]       =      os.path.split(__file__)
a		:	int       =      os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





@get_duration
def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							int							       =      dataset.select(range(0   ,	len(UpperCAmelCase_						)   ,	2						)						)





@get_duration
def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							int							       =      dataset.sort("""numbers"""						)





@get_duration
def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							List[str]							       =      dataset.shuffle()





@get_duration
def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[int]							       =      dataset.train_test_split(0.1						)





@get_duration
def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=10						):
    for shard_id in range(UpperCAmelCase_						):
        _a				:							Optional[int]							       =      dataset.shard(UpperCAmelCase_   ,	UpperCAmelCase_						)





def UpperCAmelCase    (       ):
    _a				:							int							       =      {"""num examples""": SPEED_TEST_N_EXAMPLES}
    _a				:							int							       =      (select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("""generating dataset"""						)
        _a				:							Tuple							       =      datasets.Features({"""text""": datasets.Value("""string"""						), """numbers""": datasets.Value("""float32"""						)}						)
        _a				:							Optional[int]							       =      generate_example_dataset(
            os.path.join(UpperCAmelCase_   ,	"""dataset.arrow"""						)   ,	UpperCAmelCase_   ,	num_examples=UpperCAmelCase_						)
        print("""Functions"""						)
        for func in functions:
            print(func.__name__						)
            _a				:							Optional[Any]							       =      func(UpperCAmelCase_						)

    with open(UpperCAmelCase_   ,	"""wb"""						) as f:
        f.write(json.dumps(UpperCAmelCase_						).encode("""utf-8"""						)						)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_indices_mapping()








'''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      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=13		,     __lowerCAmelCase=32		,     __lowerCAmelCase=1		,     __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=False		,     __lowerCAmelCase=2		,     ):







      '''simple docstring'''


      _a				:							str							       =      parent
      _a				:							str							       =      batch_size
      _a				:							int							       =      image_size
      _a				:							int							       =      patch_size
      _a				:							Any							       =      num_channels
      _a				:							Optional[Any]							       =      embed_dim
      _a				:							List[Any]							       =      depths
      _a				:							Union[str, Any]							       =      num_heads
      _a				:							List[Any]							       =      window_size
      _a				:							Optional[Any]							       =      mlp_ratio
      _a				:							Dict							       =      qkv_bias
      _a				:							Optional[int]							       =      hidden_dropout_prob
      _a				:							List[Any]							       =      attention_probs_dropout_prob
      _a				:							List[str]							       =      drop_path_rate
      _a				:							str							       =      hidden_act
      _a				:							Optional[int]							       =      use_absolute_embeddings
      _a				:							List[str]							       =      patch_norm
      _a				:							List[str]							       =      layer_norm_eps
      _a				:							Any							       =      initializer_range
      _a				:							Any							       =      is_training
      _a				:							Any							       =      scope
      _a				:							int							       =      use_labels
      _a				:							Union[str, Any]							       =      upscale

      # here we set some attributes to make tests pass
      _a				:							List[str]							       =      len(__lowerCAmelCase )
      _a				:							str							       =      embed_dim
      _a				:							Optional[int]							       =      (image_size // patch_size) ** 2



  def     a_							(			self ):







      '''simple docstring'''


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

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

      _a				:							str							       =      self.get_config()

      return config, pixel_values, labels



  def     a_							(			self ):







      '''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     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      SwinaSRModel(config=__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							List[str]							       =      model(__lowerCAmelCase )

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



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Dict							       =      SwinaSRForImageSuperResolution(__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							Tuple							       =      model(__lowerCAmelCase )

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



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.prepare_config_and_inputs()
      _a       , _a       , _a				:							int							       =      config_and_inputs
      _a				:							Optional[int]							       =      {"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_torch
class      UpperCamelCase       (	snake_case__      ,		snake_case__      ,		unittest.TestCase       ):
  a     :Dict          =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
  a     :List[str]          =					{'feature-extraction': SwinaSRModel} if is_torch_available() else {}

  a     :List[Any]          =					False
  a     :Tuple          =					False
  a     :Any          =					False
  a     :Union[str, Any]          =					False
  a     :Union[str, Any]          =					False



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      SwinaSRModelTester(self )
      _a				:							Optional[int]							       =      ConfigTester(self		,     config_class=__lowerCAmelCase		,     embed_dim=37 )



  def     a_							(			self ):







      '''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     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_super_resolution(*__lowerCAmelCase )



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







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not use inputs_embeds""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							int							       =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _a				:							Union[str, Any]							       =      model_class(__lowerCAmelCase )
          self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module) )
          _a				:							Optional[int]							       =      model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__lowerCAmelCase		,     nn.Linear ) )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							Tuple							       =      self.model_tester.prepare_config_and_inputs_for_common()

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

          _a				:							List[Any]							       =      ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]		,     __lowerCAmelCase )



  @slow
  def     a_							(			self ):







      '''simple docstring'''


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



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							str							       =      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				:							Any							       =      model_class(config=__lowerCAmelCase )
          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     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							Dict							       =      self.model_tester.prepare_config_and_inputs_for_common()
      _a				:							List[str]							       =      True

      for model_class in self.all_model_classes:
          _a				:							Dict							       =      True
          _a				:							Optional[int]							       =      False
          _a				:							Any							       =      True
          _a				:							Dict							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Optional[Any]							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )
          _a				:							Optional[int]							       =      outputs.attentions
          _a				:							Tuple							       =      len(self.model_tester.depths )
          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _a				:							List[str]							       =      True
          _a				:							str							       =      config.window_size**2
          _a				:							Union[str, Any]							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Any							       =      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				:							Dict							       =      len(__lowerCAmelCase )

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

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

          _a				:							List[str]							       =      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]		,     )






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



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      SwinaSRImageProcessor()
      _a				:							Optional[Any]							       =      SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64""" ).to(__lowerCAmelCase )

      _a				:							Tuple							       =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
      _a				:							Optional[int]							       =      processor(images=__lowerCAmelCase		,     return_tensors="""pt""" ).to(__lowerCAmelCase )

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

      # verify the logits
      _a				:							List[Any]							       =      torch.Size([1, 3, 976, 1296] )
      self.assertEqual(outputs.reconstruction.shape		,     __lowerCAmelCase )
      _a				:							List[str]							       =      torch.tensor(
          [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]] ).to(__lowerCAmelCase )
      self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]		,     __lowerCAmelCase		,     atol=1e-4 ) )


