
'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_mbartaa import MBartaaTokenizer
else:
						__lowercase				   =  None


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase				   =  {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

__lowercase				   =  {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
__lowercase				   =  ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					List[str]								=	VOCAB_FILES_NAMES
			UpperCAmelCase_      :					int								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_      :					Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_      :					Any								=	['input_ids', 'attention_mask']
			UpperCAmelCase_      :					Optional[Any]								=	MBartaaTokenizer

			UpperCAmelCase_      :					List[int]								=	[]
			UpperCAmelCase_      :					List[int]								=	[]



			def __init__(       self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase="</s>"	,   __lowerCAmelCase="</s>"	,   __lowerCAmelCase="<s>"	,   __lowerCAmelCase="<unk>"	,   __lowerCAmelCase="<pad>"	,   __lowerCAmelCase="<mask>"	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else mask_token

					lowerCAmelCase        =  kwargs.get("""additional_special_tokens"""	,   [])
					kwargs["additional_special_tokens"] += [
					    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    __lowerCAmelCase	,   src_lang=__lowerCAmelCase	,   tgt_lang=__lowerCAmelCase	,   tokenizer_file=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   sep_token=__lowerCAmelCase	,   cls_token=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   mask_token=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

					lowerCAmelCase        =  vocab_file
					lowerCAmelCase        =  False if not self.vocab_file else True

					lowerCAmelCase        =  {
					    lang_code: self.convert_tokens_to_ids(__lowerCAmelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
					}

					lowerCAmelCase        =  src_lang if src_lang is not None else """en_XX"""
					lowerCAmelCase        =  tgt_lang
					lowerCAmelCase        =  self.lang_code_to_id[self._src_lang]
					self.set_src_lang_special_tokens(self._src_lang)



			@property
			def 							a_	(       self):




					"""simple docstring"""


					return self._src_lang



			@src_lang.setter
			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  new_src_lang
					self.set_src_lang_special_tokens(self._src_lang)



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


					if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = "en_XX"	,   __lowerCAmelCase = None	,   __lowerCAmelCase = "ro_RO"	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  src_lang
					lowerCAmelCase        =  tgt_lang
					return super().prepare_seqaseq_batch(__lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					return self.set_src_lang_special_tokens(self.src_lang)



			def 							a_	(       self):




					"""simple docstring"""


					return self.set_tgt_lang_special_tokens(self.tgt_lang)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  self.convert_tokens_to_ids(__lowerCAmelCase)
					lowerCAmelCase        =  [self.cur_lang_code_id]
					lowerCAmelCase        =  [self.eos_token_id]

					lowerCAmelCase        =  self.convert_ids_to_tokens(self.prefix_tokens)
					lowerCAmelCase        =  self.convert_ids_to_tokens(self.suffix_tokens)

					lowerCAmelCase        =  processors.TemplateProcessing(
					    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str	,   pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str	,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,   self.prefix_tokens + self.suffix_tokens))	,   )



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  self.convert_tokens_to_ids(__lowerCAmelCase)
					lowerCAmelCase        =  [self.cur_lang_code_id]
					lowerCAmelCase        =  [self.eos_token_id]

					lowerCAmelCase        =  self.convert_ids_to_tokens(self.prefix_tokens)
					lowerCAmelCase        =  self.convert_ids_to_tokens(self.suffix_tokens)

					lowerCAmelCase        =  processors.TemplateProcessing(
					    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str	,   pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str	,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,   self.prefix_tokens + self.suffix_tokens))	,   )



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


					if src_lang is None or tgt_lang is None:
							raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""")
					lowerCAmelCase        =  src_lang
					lowerCAmelCase        =  self(__lowerCAmelCase	,   add_special_tokens=__lowerCAmelCase	,   return_tensors=__lowerCAmelCase	,   **__lowerCAmelCase)
					lowerCAmelCase        =  self.convert_tokens_to_ids(__lowerCAmelCase)
					lowerCAmelCase        =  tgt_lang_id
					return inputs







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
							    """tokenizer.""")

					if not os.path.isdir(__lowerCAmelCase):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory")
							return
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

					if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCAmelCase):
							copyfile(self.vocab_file	,   __lowerCAmelCase)

					return (out_vocab_file,)





'''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__							(      _A: Dict				,       _A: str				,       _A: int			)   ->     Any:

		'''simple docstring'''


		lowerCAmelCase        =  FNetConfig.from_json_file(_A			)
		print(f"Building PyTorch model from configuration: {config}"			)
		lowerCAmelCase        =  FNetForPreTraining(_A			)

		lowerCAmelCase        =  restore_checkpoint(_A				,       _A			)
		lowerCAmelCase        =  checkpoint_dict["""target"""]

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

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

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

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""bias"""]			)

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

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

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

		# Seq Relationship Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		lowerCAmelCase        =  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__":
						__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)




