






"""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 _lowerCAmelCase						(	__lowerCamelCase:Optional[Any]			,    __lowerCamelCase:List[Any]			,    __lowerCamelCase:List[str] ):

						'''simple docstring'''






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

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

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

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

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

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

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

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

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

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

						# Seq Relationship Layers
						__magic_name__      =  torch.tensor(
						    pretrained_model_params["classification"]["output_kernel"] )
						__magic_name__      =  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__":
			lowercase           =							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.''')
			lowercase           =							parser.parse_args()
			convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)











"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel

@require_torch
@require_sentencepiece
@require_tokenizers
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     dtype=torch.float    ,     )
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



