





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:
     '''simple docstring'''


     def __init__(						self							,    snake_case_							,    snake_case_=13							,    snake_case_=32							,    snake_case_=1							,    snake_case_=3							,    snake_case_=16							,    snake_case_=[1, 2, 1]							,    snake_case_=[2, 2, 4]							,    snake_case_=2							,    snake_case_=2.0							,    snake_case_=True							,    snake_case_=0.0							,    snake_case_=0.0							,    snake_case_=0.1							,    snake_case_="gelu"							,    snake_case_=False							,    snake_case_=True							,    snake_case_=0.02							,    snake_case_=1E-5							,    snake_case_=True							,    snake_case_=None							,    snake_case_=False							,    snake_case_=2							,    ):
         _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(snake_case_       )
         _A    =       embed_dim
         _A    =       (image_size // patch_size) ** 2




     def 					lowerCAmelCase__		(						self       ):
         _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       ):
         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							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       SwinaSRModel(config=snake_case_       )
         model.to(snake_case_       )
         model.eval()
         _A    =       model(snake_case_       )

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




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       SwinaSRForImageSuperResolution(snake_case_       )
         model.to(snake_case_       )
         model.eval()
         _A    =       model(snake_case_       )

         _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       ):
         _A    =       self.prepare_config_and_inputs()
         _A, _A, _A    =       config_and_inputs
         _A    =       {'pixel_values': pixel_values}
         return config, inputs_dict







@require_torch
class     lowerCamelCase(	__snake_case ,   __snake_case ,   unittest.TestCase  ):
     '''simple docstring'''


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

     __magic_name__        = False
     __magic_name__        = False
     __magic_name__        = False
     __magic_name__        = False
     __magic_name__        = False




     def 					lowerCAmelCase__		(						self       ):
         _A    =       SwinaSRModelTester(self       )
         _A    =       ConfigTester(self							,    config_class=snake_case_							,    embed_dim=37       )




     def 					lowerCAmelCase__		(						self       ):
         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       ):
         _A    =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_super_resolution(*snake_case_       )




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




     @unittest.skip(reason='Swin2SR does not use inputs_embeds'       )
     def 					lowerCAmelCase__		(						self       ):
         pass




     @unittest.skip(reason='Swin2SR does not support training yet'       )
     def 					lowerCAmelCase__		(						self       ):
         pass




     @unittest.skip(reason='Swin2SR does not support training yet'       )
     def 					lowerCAmelCase__		(						self       ):
         pass




     def 					lowerCAmelCase__		(						self       ):
         _A, _A    =       self.model_tester.prepare_config_and_inputs_for_common()

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




     def 					lowerCAmelCase__		(						self       ):
         _A, _A    =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _A    =       model_class(snake_case_       )
             _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]							,    snake_case_       )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A    =       SwinaSRModel.from_pretrained(snake_case_       )
             self.assertIsNotNone(snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A, _A    =       self.model_tester.prepare_config_and_inputs_for_common()

         _A    =       _config_zero_init(snake_case_       )
         for model_class in self.all_model_classes:
             _A    =       model_class(config=snake_case_       )
             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       ):
         _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(snake_case_       )
             model.to(snake_case_       )
             model.eval()
             with torch.no_grad():
                 _A    =       model(**self._prepare_for_class(snake_case_							,    snake_case_       )       )
             _A    =       outputs.attentions
             _A    =       len(self.model_tester.depths       )
             self.assertEqual(len(snake_case_       )							,    snake_case_       )

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

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

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

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

             _A    =       outputs.attentions

             self.assertEqual(len(snake_case_       )							,    snake_case_       )

             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  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         _A    =       SwinaSRImageProcessor()
         _A    =       SwinaSRForImageSuperResolution.from_pretrained('caidas/swin2SR-classical-sr-x2-64'       ).to(snake_case_       )

         _A    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
         _A    =       processor(images=snake_case_							,    return_tensors='pt'       ).to(snake_case_       )

         # forward pass
         with torch.no_grad():
             _A    =       model(**snake_case_       )

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




    """simple docstring"""




    _A    =       FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE							)
    print(F"Building PyTorch model from configuration: {config}"							)
    _A    =       FNetForPreTraining(_SCREAMING_SNAKE_CASE							)

    _A    =       restore_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _A    =       checkpoint_dict['target']

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

    _A    =       state_dict['fnet.embeddings.position_ids']
    _A    =       {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']							)

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

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

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

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

    # Pooler Layers
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']							).T
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['bias']							)

    # Masked LM Layers
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']							)
    _A    =       torch.tensor(pretrained_model_params['predictions_output']['output_bias']							)

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

    # Load State Dict
    fnet_pretraining_model.load_state_dict(_SCREAMING_SNAKE_CASE							)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"							)
    fnet_pretraining_model.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
 __A       : str						    = 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.")
 __A       : Any						    = parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





