





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 							snake_case_		(      _A    ,       unittest.TestCase):
		lowerCamelCase     :Dict							=  HerbertTokenizer
		lowerCamelCase     :List[str]							=  HerbertTokenizerFast
		lowerCamelCase     :Optional[int]							=  True







		def       __lowercase (		self )					-> Dict:
				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:
						lowerCamelCase :			Optional[Any]    		=f_data.read().replace('''\n\n'''      ,  '''\n''' ).strip()

				lowerCamelCase :			Any    		=[
				    '''<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>''',
				]
				lowerCamelCase :			int    		=dict(zip(__lowercase      ,  range(len(__lowercase ) ) ) )
				lowerCamelCase :			Optional[int]    		=['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

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







		def       __lowercase (		self      ,  __lowercase )					-> List[Any]:
				lowerCamelCase :			List[str]    		='''lower newer'''
				lowerCamelCase :			Optional[Any]    		='''lower newer'''
				return input_text, output_text







		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			Optional[Any]    		=self.tokenizer_class(vocab_file=self.vocab_file      ,  merges_file=self.merges_file )

				lowerCamelCase :			List[Any]    		='''lower'''
				lowerCamelCase :			Tuple    		=['''low''', '''er</w>''']
				lowerCamelCase :			Optional[Any]    		=tokenizer.tokenize(__lowercase )
				self.assertListEqual(__lowercase      ,  __lowercase )

				lowerCamelCase :			List[Any]    		=tokens + ['''<unk>''']
				lowerCamelCase :			Any    		=[1_6, 1_7, 2_3]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase )      ,  __lowercase )







		def       __lowercase (		self )					-> Any:
				if not self.test_rust_tokenizer:
						return

				lowerCamelCase :			Optional[int]    		=self.get_tokenizer()
				lowerCamelCase :			Dict    		=self.get_rust_tokenizer()

				lowerCamelCase :			Dict    		='''lower,newer'''

				lowerCamelCase :			Dict    		=tokenizer.tokenize(__lowercase )
				lowerCamelCase :			List[str]    		=rust_tokenizer.tokenize(__lowercase )
				self.assertListEqual(__lowercase      ,  __lowercase )

				lowerCamelCase :			Optional[int]    		=tokenizer.encode(__lowercase      ,  add_special_tokens=__lowercase )
				lowerCamelCase :			List[str]    		=rust_tokenizer.encode(__lowercase      ,  add_special_tokens=__lowercase )
				self.assertListEqual(__lowercase      ,  __lowercase )

				lowerCamelCase :			Optional[int]    		=self.get_rust_tokenizer()
				lowerCamelCase :			Dict    		=tokenizer.encode(__lowercase )
				lowerCamelCase :			Union[str, Any]    		=rust_tokenizer.encode(__lowercase )
				self.assertListEqual(__lowercase      ,  __lowercase )







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

				lowerCamelCase :			List[Any]    		=tokenizer.encode('''konstruowanie sekwencji'''      ,  add_special_tokens=__lowercase )
				lowerCamelCase :			int    		=tokenizer.encode('''konstruowanie wielu sekwencji'''      ,  add_special_tokens=__lowercase )

				lowerCamelCase :			Union[str, Any]    		=tokenizer.build_inputs_with_special_tokens(__lowercase )
				lowerCamelCase :			Optional[Any]    		=tokenizer.build_inputs_with_special_tokens(__lowercase      ,  __lowercase )

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








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__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		# Initialise PyTorch model
		lowerCamelCase :			Dict    		=FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		print(F"Building PyTorch model from configuration: {config}"		)
		lowerCamelCase :			Tuple    		=FNetForPreTraining(SCREAMING_SNAKE_CASE_		)

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

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

		lowerCamelCase :			Tuple    		=state_dict['''fnet.embeddings.position_ids''']
		lowerCamelCase :			str    		={'''fnet.embeddings.position_ids''': position_ids}
		# Embedding Layers
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']		).T
		lowerCamelCase :			List[Any]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']		)
		lowerCamelCase :			List[str]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']		)
		lowerCamelCase :			Tuple    		=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 :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''bias''']		)

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

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

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

		# Pooler Layers
		lowerCamelCase :			Any    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']		)

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

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

