
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.swin.modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
      )


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class 							A__		:
      def __init__(				self   ,  __magic_name__   ,  __magic_name__=1_3   ,  __magic_name__=3_2   ,  __magic_name__=2   ,  __magic_name__=3   ,  __magic_name__=1_6   ,  __magic_name__=[1, 2, 1]   ,  __magic_name__=[2, 2, 4]   ,  __magic_name__=2   ,  __magic_name__=2.0   ,  __magic_name__=True   ,  __magic_name__=0.0   ,  __magic_name__=0.0   ,  __magic_name__=0.1   ,  __magic_name__="gelu"   ,  __magic_name__=False   ,  __magic_name__=True   ,  __magic_name__=0.02   ,  __magic_name__=1e-5   ,  __magic_name__=True   ,  __magic_name__=None   ,  __magic_name__=True   ,  __magic_name__=1_0   ,  __magic_name__=8   ,  ):
       lowerCamelCase		:     List[Any]          =				parent
       lowerCamelCase		:     Tuple          =				batch_size
       lowerCamelCase		:     Union[str, Any]          =				image_size
       lowerCamelCase		:     List[Any]          =				patch_size
       lowerCamelCase		:     str          =				num_channels
       lowerCamelCase		:     List[str]          =				embed_dim
       lowerCamelCase		:     str          =				depths
       lowerCamelCase		:     Optional[Any]          =				num_heads
       lowerCamelCase		:     Tuple          =				window_size
       lowerCamelCase		:     Any          =				mlp_ratio
       lowerCamelCase		:     Optional[int]          =				qkv_bias
       lowerCamelCase		:     Any          =				hidden_dropout_prob
       lowerCamelCase		:     List[Any]          =				attention_probs_dropout_prob
       lowerCamelCase		:     Dict          =				drop_path_rate
       lowerCamelCase		:     int          =				hidden_act
       lowerCamelCase		:     List[Any]          =				use_absolute_embeddings
       lowerCamelCase		:     List[str]          =				patch_norm
       lowerCamelCase		:     int          =				layer_norm_eps
       lowerCamelCase		:     Union[str, Any]          =				initializer_range
       lowerCamelCase		:     Dict          =				is_training
       lowerCamelCase		:     Tuple          =				scope
       lowerCamelCase		:     Dict          =				use_labels
       lowerCamelCase		:     Any          =				type_sequence_label_size
       lowerCamelCase		:     Optional[int]          =				encoder_stride
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       lowerCamelCase		:     Optional[Any]          =				None
       if self.use_labels:
        lowerCamelCase		:     Dict          =				ids_tensor([self.batch_size]   ,  self.type_sequence_label_size				)

       lowerCamelCase		:     str          =				self.get_config()

       return config, pixel_values, labels
      def 						UpperCamelCase__							(				self				):
       return SwinConfig(
           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   ,  encoder_stride=self.encoder_stride   ,  )
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				TFSwinModel(config=__magic_name__				)
       lowerCamelCase		:     Dict          =				model(__magic_name__				)

       lowerCamelCase		:     Optional[int]          =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
       lowerCamelCase		:     List[Any]          =				int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

       self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, expected_seq_len, expected_dim)				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Union[str, Any]          =				TFSwinForMaskedImageModeling(config=__magic_name__				)
       lowerCamelCase		:     Union[str, Any]          =				model(__magic_name__				)
       self.parent.assertEqual(
           result.logits.shape   ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

       # test greyscale images
       lowerCamelCase		:     Optional[int]          =				1
       lowerCamelCase		:     int          =				TFSwinForMaskedImageModeling(__magic_name__				)

       lowerCamelCase		:     List[Any]          =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       lowerCamelCase		:     int          =				model(__magic_name__				)
       self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, 1, self.image_size, self.image_size)				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Tuple          =				self.type_sequence_label_size
       lowerCamelCase		:     Optional[Any]          =				TFSwinForImageClassification(__magic_name__				)
       lowerCamelCase		:     str          =				model(__magic_name__   ,  labels=__magic_name__				)
       self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.type_sequence_label_size)				)

       # test greyscale images
       lowerCamelCase		:     Dict          =				1
       lowerCamelCase		:     Tuple          =				TFSwinForImageClassification(__magic_name__				)
       lowerCamelCase		:     Tuple          =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       lowerCamelCase		:     Optional[Any]          =				model(__magic_name__				)
       self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.type_sequence_label_size)				)






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





@require_tf
class 							A__		(     __SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
      _UpperCAmelCase    :   int   	=      (
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      _UpperCAmelCase    :   int   	=      (
          {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )

      _UpperCAmelCase    :   List[str]   	=      False
      _UpperCAmelCase    :   Union[str, Any]   	=      False
      _UpperCAmelCase    :   Union[str, Any]   	=      False
      _UpperCAmelCase    :   Tuple   	=      False
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				TFSwinModelTester(self				)
       lowerCamelCase		:     Optional[Any]          =				ConfigTester(self   ,  config_class=__magic_name__   ,  embed_dim=3_7				)
      def 						UpperCamelCase__							(				self				):
       self.create_and_test_config_common_properties()
       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				):
       return
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[Any]          =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__magic_name__				)
      @unittest.skip(reason="""Swin does not use inputs_embeds"""				)
      def 						UpperCamelCase__							(				self				):
       pass
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowerCamelCase		:     Tuple          =				model_class(__magic_name__				)
        self.assertIsInstance(model.get_input_embeddings()   ,  tf.keras.layers.Layer				)
        lowerCamelCase		:     int          =				model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__magic_name__   ,  tf.keras.layers.Dense				)				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase      ,   lowerCamelCase		:     List[str]          =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowerCamelCase		:     Tuple          =				model_class(__magic_name__				)
        lowerCamelCase		:     str          =				inspect.signature(model.call				)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase		:     List[Any]          =				[*signature.parameters.keys()]

        lowerCamelCase		:     Tuple          =				["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,  __magic_name__				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase      ,   lowerCamelCase		:     Any          =				self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase		:     str          =				True

       for model_class in self.all_model_classes:
        lowerCamelCase		:     Union[str, Any]          =				True
        lowerCamelCase		:     Optional[Any]          =				False
        lowerCamelCase		:     Tuple          =				True
        lowerCamelCase		:     Optional[Any]          =				model_class(__magic_name__				)
        lowerCamelCase		:     List[Any]          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)
        lowerCamelCase		:     int          =				outputs.attentions
        lowerCamelCase		:     str          =				len(self.model_tester.depths				)
        self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        lowerCamelCase		:     Union[str, Any]          =				True
        lowerCamelCase		:     List[Any]          =				config.window_size**2
        lowerCamelCase		:     Union[str, Any]          =				model_class(__magic_name__				)
        lowerCamelCase		:     List[str]          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)
        lowerCamelCase		:     Union[str, Any]          =				outputs.attentions
        self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

        self.assertListEqual(
            list(attentions[0].shape[-3:]				)   ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,  )
        lowerCamelCase		:     Optional[Any]          =				len(__magic_name__				)

        # Check attention is always last and order is fine
        lowerCamelCase		:     List[Any]          =				True
        lowerCamelCase		:     Union[str, Any]          =				True
        lowerCamelCase		:     List[Any]          =				model_class(__magic_name__				)
        lowerCamelCase		:     Any          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)

        if hasattr(self.model_tester   ,  """num_hidden_states_types"""				):
         lowerCamelCase		:     Optional[int]          =				self.model_tester.num_hidden_states_types
        else:
         # also another +1 for reshaped_hidden_states
         lowerCamelCase		:     Optional[Any]          =				2
        self.assertEqual(out_len + added_hidden_states   ,  len(__magic_name__				)				)

        lowerCamelCase		:     Any          =				outputs.attentions

        self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

        self.assertListEqual(
            list(self_attentions[0].shape[-3:]				)   ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,  )
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				model_class(__magic_name__				)
       lowerCamelCase		:     Any          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)
       lowerCamelCase		:     Dict          =				outputs.hidden_states

       lowerCamelCase		:     str          =				getattr(
           self.model_tester   ,  """expected_num_hidden_layers"""   ,  len(self.model_tester.depths				) + 1				)
       self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

       # Swin has a different seq_length
       lowerCamelCase		:     Any          =				to_atuple(config.patch_size				)

       lowerCamelCase		:     Union[str, Any]          =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]				)   ,  [num_patches, self.model_tester.embed_dim]   ,  )

       lowerCamelCase		:     int          =				outputs.reshaped_hidden_states
       self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

       lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     List[str]          =				reshaped_hidden_states[0].shape

       lowerCamelCase		:     Dict          =				tf.reshape(reshaped_hidden_states[0]   ,  (batch_size, num_channels, height * width)				)
       lowerCamelCase		:     str          =				tf.transpose(__magic_name__   ,  (0, 2, 1)				)

       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]				)   ,  [num_patches, self.model_tester.embed_dim]   ,  )
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				self.model_tester.prepare_config_and_inputs_for_common()

       lowerCamelCase		:     Dict          =				to_atuple(self.model_tester.image_size				)

       for model_class in self.all_model_classes:
        lowerCamelCase		:     List[str]          =				True
        self.check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowerCamelCase		:     Dict          =				True

        self.check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase      ,   lowerCamelCase		:     int          =				self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase		:     List[str]          =				3

       lowerCamelCase		:     Optional[int]          =				to_atuple(self.model_tester.image_size				)
       lowerCamelCase		:     Optional[Any]          =				to_atuple(config.patch_size				)

       lowerCamelCase		:     Optional[int]          =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       lowerCamelCase		:     Any          =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
        lowerCamelCase		:     List[str]          =				True
        self.check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  (padded_height, padded_width)				)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowerCamelCase		:     Optional[Any]          =				True
        self.check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  (padded_height, padded_width)				)






      @slow
      def 						UpperCamelCase__							(				self				):
       for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase		:     Union[str, Any]          =				TFSwinModel.from_pretrained(__magic_name__				)
        self.assertIsNotNone(__magic_name__				)





@require_vision
@require_tf
class 							A__		(     unittest.TestCase):
      @cached_property
      def 						UpperCamelCase__							(				self				):
       return (
           AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""				)
           if is_vision_available()
           else None
       )






      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""				)
       lowerCamelCase		:     Dict          =				self.default_image_processor

       lowerCamelCase		:     Tuple          =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
       lowerCamelCase		:     List[str]          =				image_processor(images=__magic_name__   ,  return_tensors="""tf"""				)

       # forward pass
       lowerCamelCase		:     Optional[Any]          =				model(__magic_name__				)

       # verify the logits
       lowerCamelCase		:     Union[str, Any]          =				tf.TensorShape((1, 1_0_0_0)				)
       self.assertEqual(outputs.logits.shape   ,  __magic_name__				)
       lowerCamelCase		:     Any          =				tf.constant([-0.0_948, -0.6_454, -0.0_921]				)
       self.assertTrue(np.allclose(outputs.logits[0, :3]   ,  __magic_name__   ,  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    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 # Initialise PyTorch model
 lowerCamelCase		:     List[str]          =				FNetConfig.from_json_file(lowerCamelCase							)
 print(F'''Building PyTorch model from configuration: {config}'''							)
 lowerCamelCase		:     Any          =				FNetForPreTraining(lowerCamelCase							)

 lowerCamelCase		:     List[Any]          =				restore_checkpoint(lowerCamelCase,     lowerCamelCase							)
 lowerCamelCase		:     List[str]          =				checkpoint_dict["""target"""]

 # Embeddings
 # Position IDs
 lowerCamelCase		:     Optional[Any]          =				fnet_pretraining_model.state_dict()

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

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

  lowerCamelCase		:     int          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]							).T
  lowerCamelCase		:     List[str]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]							)

  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]							).T
  lowerCamelCase		:     str          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]							)

  lowerCamelCase		:     Tuple          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Optional[int]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]							)

 # Pooler Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]							).T
 lowerCamelCase		:     List[Any]          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]							)

 # Masked LM Layers
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""kernel"""]							).T
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""bias"""]							)
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""bias"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""predictions_output"""]["""output_bias"""]							)
 lowerCamelCase		:     Any          =				torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]							)

 # Seq Relationship Layers
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_kernel"""]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_bias"""]							)

 # Load State Dict
 fnet_pretraining_model.load_state_dict(lowerCamelCase							)

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


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




