
"""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  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       Tuple		, snake_case__				:       Tuple       ):
	# Initialise PyTorch model
	A  				=				FNetConfig.from_json_file(snake_case__       )
	print(F'Building PyTorch model from configuration: {config}'       )
	A  				=				FNetForPreTraining(snake_case__       )

	A  				=				restore_checkpoint(snake_case__		, 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(snake_case__       )

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


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 json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      List[str] =      HerbertTokenizer
						_lowerCamelCase:      Any =      HerbertTokenizerFast
						_lowerCamelCase:      Optional[int] =      True


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Tuple:
							super().setUp()

							# Use a simpler test file without japanese/chinese characters
							with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'					,encoding='utf-8' ) as f_data:
								A  				=				f_data.read().replace('\n\n'					,'\n' ).strip()

							A  				=				[
							    '<s>',
							    '</s>',
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    ',</w>',
							    '<unk>',
							]
							A  				=				dict(zip(A_					,range(len(A_ ) ) ) )
							A  				=				['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['vocab_file'] )
							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['merges_file'] )
							with open(self.vocab_file					,'w' ) as fp:
								fp.write(json.dumps(A_ ) )
							with open(self.merges_file					,'w' ) as fp:
								fp.write('\n'.join(A_ ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : List[str] )       ->				int:
							A  				=				'lower newer'
							A  				=				'lower newer'
							return input_text, output_text


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							A  				=				self.tokenizer_class(vocab_file=self.vocab_file					,merges_file=self.merges_file )

							A  				=				'lower'
							A  				=				['low', 'er</w>']
							A  				=				tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokens + ['<unk>']
							A  				=				[16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Optional[int]:
							if not self.test_rust_tokenizer:
								return

							A  				=				self.get_tokenizer()
							A  				=				self.get_rust_tokenizer()

							A  				=				'lower,newer'

							A  				=				tokenizer.tokenize(A_ )
							A  				=				rust_tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokenizer.encode(A_					,add_special_tokens=A_ )
							A  				=				rust_tokenizer.encode(A_					,add_special_tokens=A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				self.get_rust_tokenizer()
							A  				=				tokenizer.encode(A_ )
							A  				=				rust_tokenizer.encode(A_ )
							self.assertListEqual(A_					,A_ )




						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[Any]:
							A  				=				self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

							A  				=				tokenizer.encode('konstruowanie sekwencji'					,add_special_tokens=A_ )
							A  				=				tokenizer.encode('konstruowanie wielu sekwencji'					,add_special_tokens=A_ )

							A  				=				tokenizer.build_inputs_with_special_tokens(A_ )
							A  				=				tokenizer.build_inputs_with_special_tokens(A_					,A_ )

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + text_a + [2]