




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        lowerCamelCase__:
      def __init__(							self:					List[str]       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					Optional[int]=13       ,			UpperCamelCase_:					Tuple=32       ,			UpperCamelCase_:					Tuple=1       ,			UpperCamelCase_:					int=3       ,			UpperCamelCase_:					int=16       ,			UpperCamelCase_:					Union[str, Any]=[1, 2, 1]       ,			UpperCamelCase_:					List[str]=[2, 2, 4]       ,			UpperCamelCase_:					Optional[Any]=2       ,			UpperCamelCase_:					Dict=2.0       ,			UpperCamelCase_:					Optional[Any]=True       ,			UpperCamelCase_:					str=0.0       ,			UpperCamelCase_:					Tuple=0.0       ,			UpperCamelCase_:					int=0.1       ,			UpperCamelCase_:					Optional[Any]="gelu"       ,			UpperCamelCase_:					Union[str, Any]=False       ,			UpperCamelCase_:					Optional[Any]=True       ,			UpperCamelCase_:					int=0.02       ,			UpperCamelCase_:					List[Any]=1E-5       ,			UpperCamelCase_:					Optional[Any]=True       ,			UpperCamelCase_:					str=None       ,			UpperCamelCase_:					List[str]=False       ,			UpperCamelCase_:					Tuple=2       ,			):
             __lowerCamelCase  =		parent
             __lowerCamelCase  =		batch_size
             __lowerCamelCase  =		image_size
             __lowerCamelCase  =		patch_size
             __lowerCamelCase  =		num_channels
             __lowerCamelCase  =		embed_dim
             __lowerCamelCase  =		depths
             __lowerCamelCase  =		num_heads
             __lowerCamelCase  =		window_size
             __lowerCamelCase  =		mlp_ratio
             __lowerCamelCase  =		qkv_bias
             __lowerCamelCase  =		hidden_dropout_prob
             __lowerCamelCase  =		attention_probs_dropout_prob
             __lowerCamelCase  =		drop_path_rate
             __lowerCamelCase  =		hidden_act
             __lowerCamelCase  =		use_absolute_embeddings
             __lowerCamelCase  =		patch_norm
             __lowerCamelCase  =		layer_norm_eps
             __lowerCamelCase  =		initializer_range
             __lowerCamelCase  =		is_training
             __lowerCamelCase  =		scope
             __lowerCamelCase  =		use_labels
             __lowerCamelCase  =		upscale

             # here we set some attributes to make tests pass
             __lowerCamelCase  =		len(UpperCamelCase_  )
             __lowerCamelCase  =		embed_dim
             __lowerCamelCase  =		(image_size // patch_size) ** 2
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             __lowerCamelCase  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

             __lowerCamelCase  =		self.get_config()

             return config, pixel_values, labels
      def      lowerCAmelCase__		(							self:					Union[str, 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      lowerCAmelCase__		(							self:					Union[str, Any]       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					List[str]       ,			UpperCamelCase_:					List[str]  ):
             __lowerCamelCase  =		SwinaSRModel(config=UpperCamelCase_  )
             model.to(UpperCamelCase_  )
             model.eval()
             __lowerCamelCase  =		model(UpperCamelCase_  )

             self.parent.assertEqual(
                 result.last_hidden_state.shape       ,			(self.batch_size, self.embed_dim, self.image_size, self.image_size)  )
      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					Tuple       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					str  ):
             __lowerCamelCase  =		SwinaSRForImageSuperResolution(UpperCamelCase_  )
             model.to(UpperCamelCase_  )
             model.eval()
             __lowerCamelCase  =		model(UpperCamelCase_  )

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





@require_torch
class        lowerCamelCase__(   __lowerCamelCase					,	__lowerCamelCase					,	unittest.TestCase):
      UpperCAmelCase__ :      List[str]       						=  (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      UpperCAmelCase__ :      int       						=  {'feature-extraction': SwinaSRModel} if is_torch_available() else {}

      UpperCAmelCase__ :      Union[str, Any]       						=  False
      UpperCAmelCase__ :      List[str]       						=  False
      UpperCAmelCase__ :      Tuple       						=  False
      UpperCAmelCase__ :      List[str]       						=  False
      UpperCAmelCase__ :      Optional[int]       						=  False
      def      lowerCAmelCase__		(							self:					Any  ):
             __lowerCamelCase  =		SwinaSRModelTester(self  )
             __lowerCamelCase  =		ConfigTester(self       ,			config_class=UpperCamelCase_       ,			embed_dim=37  )
      def      lowerCAmelCase__		(							self:					Dict  ):
             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:					Optional[Any]  ):
             __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_super_resolution(*UpperCamelCase_  )
      @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""  )
      def      lowerCAmelCase__		(							self:					Tuple  ):
             pass
      @unittest.skip(reason="""Swin2SR does not use inputs_embeds"""  )
      def      lowerCAmelCase__		(							self:					Tuple  ):
             pass
      @unittest.skip(reason="""Swin2SR does not support training yet"""  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             pass
      @unittest.skip(reason="""Swin2SR does not support training yet"""  )
      def      lowerCAmelCase__		(							self:					Tuple  ):
             pass
      def      lowerCAmelCase__		(							self:					List[str]  ):
             __lowerCamelCase,  __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    __lowerCamelCase  =		model_class(UpperCamelCase_  )
                    self.assertIsInstance(model.get_input_embeddings()       ,			(nn.Module)  )
                    __lowerCamelCase  =		model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(UpperCamelCase_       ,			nn.Linear  )  )
      def      lowerCAmelCase__		(							self:					Optional[int]  ):
             __lowerCamelCase,  __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs_for_common()

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

                    __lowerCamelCase  =		["""pixel_values"""]
                    self.assertListEqual(arg_names[:1]       ,			UpperCamelCase_  )
      @slow
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __lowerCamelCase  =		SwinaSRModel.from_pretrained(UpperCamelCase_  )
                    self.assertIsNotNone(UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase,  __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs_for_common()

             __lowerCamelCase  =		_config_zero_init(UpperCamelCase_  )
             for model_class in self.all_model_classes:
                    __lowerCamelCase  =		model_class(config=UpperCamelCase_  )
                    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:					Union[str, Any]  ):
             __lowerCamelCase,  __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs_for_common()
             __lowerCamelCase  =		True

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

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

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

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

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

                    __lowerCamelCase  =		outputs.attentions

                    self.assertEqual(len(UpperCamelCase_  )       ,			UpperCamelCase_  )

                    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        lowerCamelCase__(   unittest.TestCase):
      def      lowerCAmelCase__		(							self:					Any  ):
             __lowerCamelCase  =		SwinaSRImageProcessor()
             __lowerCamelCase  =		SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64"""  ).to(UpperCamelCase_  )

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

             # forward pass
             with torch.no_grad():
                    __lowerCamelCase  =		model(**UpperCamelCase_  )

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







import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def    lowerCamelCase__			(		A__			:   int    ,     A__			:   Tuple    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		FNetConfig.from_json_file(A__      )
       print(f'Building PyTorch model from configuration: {config}'      )
       __lowerCamelCase  =		FNetForPreTraining(A__      )

       __lowerCamelCase  =		restore_checkpoint(A__    ,     A__      )
       __lowerCamelCase  =		checkpoint_dict["""target"""]

       # Embeddings
       # Position IDs
       __lowerCamelCase  =		fnet_pretraining_model.state_dict()

       __lowerCamelCase  =		state_dict["""fnet.embeddings.position_ids"""]
       __lowerCamelCase  =		{"""fnet.embeddings.position_ids""": position_ids}
       # Embedding Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]      )

       # Encoder Layers
       for layer in range(config.num_hidden_layers      ):
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""mixing_layer_norm"""]["""scale"""]      )
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""mixing_layer_norm"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""scale"""]      )
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""bias"""]      )

       # Pooler Layers
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]      )

       # Masked LM Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]      )

       # Seq Relationship Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""classification"""]["""output_kernel"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""classification"""]["""output_bias"""]      )

       # Load State Dict
       fnet_pretraining_model.load_state_dict(A__      )

       # Save PreTrained
       print(f'Saving pretrained model to {save_path}'      )
       fnet_pretraining_model.save_pretrained(A__      )


if __name__ == "__main__":
       UpperCAmelCase_   							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--fnet_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained FNet model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

