


"""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 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							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	,					)   ->	Optional[Any]:







								'''simple docstring'''




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

								# here we set some attributes to make tests pass
								snake_case__  :						Any		=      len(lowerCamelCase__)
								snake_case__  :						List[Any]		=      embed_dim
								snake_case__  :						List[str]		=      (image_size // patch_size) ** 2




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




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

								snake_case__  :						Dict		=      None
								if self.use_labels:
									snake_case__  :						List[str]		=      ids_tensor([self.batch_size]	,					self.type_sequence_label_size)

								snake_case__  :						str		=      self.get_config()

								return config, pixel_values, labels




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







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      SwinaSRModel(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						Optional[Any]		=      model(lowerCamelCase__)

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




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







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      SwinaSRForImageSuperResolution(lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						List[str]		=      model(lowerCamelCase__)

								snake_case__  :						str		=      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]:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.prepare_config_and_inputs()
								snake_case__,					snake_case__,					snake_case__  :						str		=      config_and_inputs
								snake_case__  :						List[str]		=      {"pixel_values": pixel_values}
								return config, inputs_dict


@require_torch
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     List[str]				  =  (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
							__magic_name__					:     List[Any]				  =  {'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

							__magic_name__					:     Any				  =  False
							__magic_name__					:     str				  =  False
							__magic_name__					:     Optional[Any]				  =  False
							__magic_name__					:     List[Any]				  =  False
							__magic_name__					:     Any				  =  False




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						Tuple		=      SwinaSRModelTester(self)
								snake_case__  :						Union[str, Any]		=      ConfigTester(self	,					config_class=lowerCamelCase__	,					embed_dim=37)




							def        UpperCAmelCase (						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        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						List[str]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      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        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								pass




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







								'''simple docstring'''




								pass




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







								'''simple docstring'''




								pass




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







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




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

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




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__,					snake_case__  :						Optional[Any]		=      self.model_tester.prepare_config_and_inputs_for_common()

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

									snake_case__  :						Dict		=      ["pixel_values"]
									self.assertListEqual(arg_names[:1]	,					lowerCamelCase__)




							@slow
							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									snake_case__  :						Union[str, Any]		=      SwinaSRModel.from_pretrained(lowerCamelCase__)
									self.assertIsNotNone(lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




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

								snake_case__  :						List[Any]		=      _config_zero_init(lowerCamelCase__)
								for model_class in self.all_model_classes:
									snake_case__  :						List[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        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__,					snake_case__  :						int		=      self.model_tester.prepare_config_and_inputs_for_common()
								snake_case__  :						int		=      True

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

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

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

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

									snake_case__  :						Dict		=      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 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




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

								snake_case__  :						str		=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
								snake_case__  :						int		=      processor(images=lowerCamelCase__	,					return_tensors="pt").to(lowerCamelCase__)

								# forward pass
								with torch.no_grad():
									snake_case__  :						List[str]		=      model(**lowerCamelCase__)

								# verify the logits
								snake_case__  :						List[str]		=      torch.Size([1, 3, 976, 1_296])
								self.assertEqual(outputs.reconstruction.shape	,					lowerCamelCase__)
								snake_case__  :						Any		=      torch.tensor(
								    [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]).to(lowerCamelCase__)
								self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]	,					lowerCamelCase__	,					atol=1E-4))








import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter



class     lowercase_       (    TensorFormatter[Mapping, np.ndarray, Mapping]	):



      def __init__(     self :							Union[str, Any]     ,   A__ :							Dict=None     ,   **A__ :							str						)       ->	Any:
        super().__init__(features=A__						)
        _snake_case		=				np_array_kwargs



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							Dict						)       ->	Dict:
        if isinstance(A__     ,   A__						):
          if column and all(
              isinstance(A__     ,   np.ndarray						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
            return np.stack(A__						)
          else:
            # don't use np.array(column, dtype=object)
            # since it fails in certain cases
            # see https://stackoverflow.com/q/51005699
            _snake_case		=				np.empty(len(A__						)     ,   dtype=A__						)
            _snake_case		=				column
            return out
        return column



      def 		UpperCamelCase_						(     self :							Tuple     ,   A__ :							Optional[int]						)       ->	Optional[Any]:
        if isinstance(A__     ,   (str, bytes, type(A__						))						):
          return value
        elif isinstance(A__     ,   (np.character, np.ndarray)						) and np.issubdtype(value.dtype     ,   np.character						):
          return value
        elif isinstance(A__     ,   np.number						):
          return value

        _snake_case		=				{}

        if isinstance(A__     ,   np.ndarray						) and np.issubdtype(value.dtype     ,   np.integer						):
          _snake_case		=				{'''dtype''': np.intaa}
        elif isinstance(A__     ,   np.ndarray						) and np.issubdtype(value.dtype     ,   np.floating						):
          _snake_case		=				{'''dtype''': np.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(A__     ,   PIL.Image.Image						):
            return np.asarray(A__     ,   **self.np_array_kwargs						)

        return np.asarray(A__     ,   **{**default_dtype, **self.np_array_kwargs}						)



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Union[str, Any]						)       ->	int:
        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
          import torch

          if isinstance(A__     ,   torch.Tensor						):
            return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
        if hasattr(A__     ,   '''__array__'''						) and not isinstance(A__     ,   (np.ndarray, np.character, np.number)						):
          _snake_case		=				data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(A__     ,   np.ndarray						):
          if data_struct.dtype == object:
            return self._consolidate([self.recursive_tensorize(A__						) for substruct in data_struct]						)
        if isinstance(A__     ,   (list, tuple)						):
          return self._consolidate([self.recursive_tensorize(A__						) for substruct in data_struct]						)
        return self._tensorize(A__						)



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							dict						)       ->	Tuple:
        return map_nested(self._recursive_tensorize     ,   A__     ,   map_list=A__						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							pa.Table						)       ->	Mapping:
        _snake_case		=				self.numpy_arrow_extractor().extract_row(A__						)
        _snake_case		=				self.python_features_decoder.decode_row(A__						)
        return self.recursive_tensorize(A__						)



      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							pa.Table						)       ->	np.ndarray:
        _snake_case		=				self.numpy_arrow_extractor().extract_column(A__						)
        _snake_case		=				self.python_features_decoder.decode_column(A__     ,   pa_table.column_names[0]						)
        _snake_case		=				self.recursive_tensorize(A__						)
        _snake_case		=				self._consolidate(A__						)
        return column







      def 		UpperCamelCase_						(     self :							Dict     ,   A__ :							pa.Table						)       ->	Mapping:
        _snake_case		=				self.numpy_arrow_extractor().extract_batch(A__						)
        _snake_case		=				self.python_features_decoder.decode_batch(A__						)
        _snake_case		=				self.recursive_tensorize(A__						)
        for column_name in batch:
          _snake_case		=				self._consolidate(batch[column_name]						)
        return batch

