

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_ (			_A	,				unittest.TestCase):
 _lowerCAmelCase									=	HerbertTokenizer
 _lowerCAmelCase									=	HerbertTokenizerFast
 _lowerCAmelCase									=	True




 def     a       (			self							):
   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:
     __UpperCAmelCase	:  Optional[Any]		=     f_data.read().replace('\n\n'  ,  '\n'							).strip()

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

   __UpperCAmelCase	:  List[str]		=     os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['vocab_file']							)
   __UpperCAmelCase	:  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     a       (			self  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  List[str]		=     '''lower newer'''
   __UpperCAmelCase	:  Optional[Any]		=     '''lower newer'''
   return input_text, output_text




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     self.tokenizer_class(vocab_file=self.vocab_file  ,  merges_file=self.merges_file							)

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

   __UpperCAmelCase	:  List[Any]		=     tokens + ['''<unk>''']
   __UpperCAmelCase	:  Any		=     [16, 17, 23]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase							)  ,  __lowercase							)




 def     a       (			self							):
   if not self.test_rust_tokenizer:
     return

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

   __UpperCAmelCase	:  Dict		=     '''lower,newer'''

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

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

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




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'							)

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

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

