

"""simple docstring"""





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 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	# Initialise PyTorch model
	lowerCAmelCase__			:		List[str]						       =			FNetConfig.from_json_file(A_				)
	print(f'Building PyTorch model from configuration: {config}'				)
	lowerCAmelCase__			:		List[Any]						       =			FNetForPreTraining(A_				)

	lowerCAmelCase__			:		Tuple						       =			restore_checkpoint(A_		,  A_				)
	lowerCAmelCase__			:		Tuple						       =			checkpoint_dict['''target''']

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

	lowerCAmelCase__			:		str						       =			state_dict['''fnet.embeddings.position_ids''']
	lowerCAmelCase__			:		List[Any]						       =			{'''fnet.embeddings.position_ids''': position_ids}
	# Embedding Layers
	lowerCAmelCase__			:		Optional[int]						       =			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']				)
	lowerCAmelCase__			:		Union[str, Any]						       =			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]				)
	lowerCAmelCase__			:		Any						       =			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']				)
	lowerCAmelCase__			:		Optional[Any]						       =			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']				).T
	lowerCAmelCase__			:		Optional[Any]						       =			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']				)
	lowerCAmelCase__			:		Union[str, Any]						       =			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']				)
	lowerCAmelCase__			:		str						       =			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']				)

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

		lowerCAmelCase__			:		Optional[int]						       =			torch.tensor(
		    pretrained_model_params['''encoder'''][f'feed_forward_{layer}']['''intermediate''']['''kernel''']				).T
		lowerCAmelCase__			:		str						       =			torch.tensor(
		    pretrained_model_params['''encoder'''][f'feed_forward_{layer}']['''intermediate''']['''bias''']				)

		lowerCAmelCase__			:		Union[str, Any]						       =			torch.tensor(
		    pretrained_model_params['''encoder'''][f'feed_forward_{layer}']['''output''']['''kernel''']				).T
		lowerCAmelCase__			:		Union[str, Any]						       =			torch.tensor(
		    pretrained_model_params['''encoder'''][f'feed_forward_{layer}']['''output''']['''bias''']				)

		lowerCAmelCase__			:		Optional[int]						       =			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__			:		Union[str, Any]						       =			torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']				).T
	lowerCAmelCase__			:		List[Any]						       =			torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']				)

	# Masked LM Layers
	lowerCAmelCase__			:		Any						       =			torch.tensor(
	    pretrained_model_params['''predictions_dense''']['''kernel''']				).T
	lowerCAmelCase__			:		Union[str, Any]						       =			torch.tensor(
	    pretrained_model_params['''predictions_dense''']['''bias''']				)
	lowerCAmelCase__			:		List[Any]						       =			torch.tensor(
	    pretrained_model_params['''predictions_layer_norm''']['''scale''']				)
	lowerCAmelCase__			:		Tuple						       =			torch.tensor(
	    pretrained_model_params['''predictions_layer_norm''']['''bias''']				)
	lowerCAmelCase__			:		str						       =			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']				)
	lowerCAmelCase__			:		List[Any]						       =			torch.tensor(
	    pretrained_model_params['''predictions_output''']['''output_bias''']				)
	lowerCAmelCase__			:		Union[str, Any]						       =			torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']				)

	# Seq Relationship Layers
	lowerCAmelCase__			:		List[str]						       =			torch.tensor(
	    pretrained_model_params['''classification''']['''output_kernel''']				)
	lowerCAmelCase__			:		str						       =			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						:   int							   =	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						:   Union[str, Any]							   =	parser.parse_args()
							convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)







"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import SwinaSRImageProcessor
							from transformers.image_transforms import get_image_size







class        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



					def __init__( self    :       Dict     ,lowercase_    :       List[str]     ,lowercase_    :       List[str]=7     ,lowercase_    :       List[Any]=3     ,lowercase_    :       str=1_8     ,lowercase_    :       Any=3_0     ,lowercase_    :       int=4_0_0     ,lowercase_    :       Tuple=True     ,lowercase_    :       Optional[int]=1 / 2_5_5     ,lowercase_    :       int=True     ,lowercase_    :       Union[str, Any]=8     ,):
						lowerCAmelCase__			:		Tuple						       =			parent
						lowerCAmelCase__			:		Optional[Any]						       =			batch_size
						lowerCAmelCase__			:		int						       =			num_channels
						lowerCAmelCase__			:		Union[str, Any]						       =			image_size
						lowerCAmelCase__			:		Optional[Any]						       =			min_resolution
						lowerCAmelCase__			:		Union[str, Any]						       =			max_resolution
						lowerCAmelCase__			:		int						       =			do_rescale
						lowerCAmelCase__			:		Tuple						       =			rescale_factor
						lowerCAmelCase__			:		Dict						       =			do_pad
						lowerCAmelCase__			:		str						       =			pad_size







					def  __lowerCAmelCase				( self    :       int	):
						return {
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						    "pad_size": self.pad_size,
						}







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       List[str]=False     ,lowercase_    :       Any=False     ,lowercase_    :       Dict=False	):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
							lowerCAmelCase__			:		int						       =			[]
							for i in range(self.batch_size	):
								image_inputs.append(
								    np.random.randint(
								        2_5_5     ,size=(self.num_channels, self.max_resolution, self.max_resolution)     ,dtype=np.uinta	)	)
						else:
							lowerCAmelCase__			:		str						       =			[]
							for i in range(self.batch_size	):
								lowerCAmelCase__       ,lowerCAmelCase__			:		Any						       =			np.random.choice(np.arange(self.min_resolution     ,self.max_resolution	)     ,2	)
								image_inputs.append(np.random.randint(2_5_5     ,size=(self.num_channels, width, height)     ,dtype=np.uinta	)	)

						if not numpify and not torchify:
							# PIL expects the channel dimension as last dimension
							lowerCAmelCase__			:		Optional[int]						       =			[Image.fromarray(np.moveaxis(lowercase_     ,0     ,-1	)	) for x in image_inputs]

						if torchify:
							lowerCAmelCase__			:		int						       =			[torch.from_numpy(lowercase_	) for x in image_inputs]

						return image_inputs







@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE      (	a_					,							unittest.TestCase ):






					"""simple docstring"""



					lowercase__       				= SwinaSRImageProcessor if is_vision_available() else None







					def  __lowerCAmelCase				( self    :       Dict	):
						lowerCAmelCase__			:		Dict						       =			SwinaSRImageProcessingTester(self	)







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return self.image_processor_tester.prepare_image_processor_dict()







					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowercase_     ,'''do_rescale'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''rescale_factor'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''do_pad'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''pad_size'''	)	)







					def  __lowerCAmelCase				( self    :       Dict	):
						pass







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       Optional[Any]	):
						lowerCAmelCase__       ,lowerCAmelCase__			:		Tuple						       =			get_image_size(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			self.image_processor_tester.pad_size

						lowerCAmelCase__			:		List[Any]						       =			(old_height // size + 1) * size - old_height
						lowerCAmelCase__			:		Union[str, Any]						       =			(old_width // size + 1) * size - old_width
						return old_height + pad_height, old_width + pad_width







					def  __lowerCAmelCase				( self    :       List[Any]	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						lowerCAmelCase__			:		int						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,Image.Image	)

						# Test not batched input
						lowerCAmelCase__			:		List[str]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			self.calculate_expected_size(np.array(image_inputs[0]	)	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







					def  __lowerCAmelCase				( self    :       int	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						lowerCAmelCase__			:		Tuple						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,numpify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,np.ndarray	)

						# Test not batched input
						lowerCAmelCase__			:		Optional[Any]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







					def  __lowerCAmelCase				( self    :       Dict	):
						# Initialize image_processor
						lowerCAmelCase__			:		Tuple						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						lowerCAmelCase__			:		Union[str, Any]						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,torchify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,torch.Tensor	)

						# Test not batched input
						lowerCAmelCase__			:		Tuple						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		int						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)




