


"""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    a__       ( __lowercase   ,	__lowercase   ,	__lowercase      )		->		Optional[Any]:
				# Initialise PyTorch model
				_A         =	FNetConfig.from_json_file(__lowercase      )
				print(f"""Building PyTorch model from configuration: {config}"""      )
				_A         =	FNetForPreTraining(__lowercase      )

				_A         =	restore_checkpoint(__lowercase   ,	__lowercase      )
				_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(__lowercase      )

				# Save PreTrained
				print(f"""Saving pretrained model to {save_path}"""      )
				fnet_pretraining_model.save_pretrained(__lowercase      )


if __name__ == "__main__":
		a_											=			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.")
		a_											=			parser.parse_args()
		convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							lowerCamelCase_  :       Optional[Any]					      =     None


lowerCamelCase_  :       str					      =     logging.get_logger(__name__)

lowerCamelCase_  :       List[str]					      =     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_  :       List[str]					      =     {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_  :       Dict					      =     {
    """facebook/xglm-564M""": 20_48,
}

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Optional[Any]    					=       VOCAB_FILES_NAMES
			__UpperCamelCase					:  int    					=       PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase					:  List[str]    					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase					:  Dict    					=       ["""input_ids""", """attention_mask"""]
			__UpperCamelCase					:  Union[str, Any]    					=       XGLMTokenizer

			def __init__(				self       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=None  ,		snake_case_       :				Any=None  ,		snake_case_       :				Optional[Any]="<s>"  ,		snake_case_       :				Union[str, Any]="</s>"  ,		snake_case_       :				Optional[Any]="</s>"  ,		snake_case_       :				Dict="<s>"  ,		snake_case_       :				int="<unk>"  ,		snake_case_       :				Dict="<pad>"  ,		**snake_case_       :				List[str]  ,		):
							# Compatibility with the original tokenizer
							UpperCamelCase_:							List[Any]		=  7
							UpperCamelCase_:							int		=  [f'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

							UpperCamelCase_:							Any		=  kwargs.get("""additional_special_tokens"""  ,		[]					)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    snake_case_  ,		tokenizer_file=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		unk_token=snake_case_  ,		pad_token=snake_case_  ,		**snake_case_  ,		)

							UpperCamelCase_:							Dict		=  vocab_file
							UpperCamelCase_:							Union[str, Any]		=  False if not self.vocab_file else True

			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):

							if token_ids_a is None:
											return [self.sep_token_id] + token_ids_a
							UpperCamelCase_:							Optional[Any]		=  [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a

			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):
							UpperCamelCase_:							Any		=  [self.sep_token_id]

							if token_ids_a is None:
											return len(sep + token_ids_a					) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a					) * [0]




			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				str  ,		snake_case_       :				Optional[str] = None					):
							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(snake_case_					):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''					)
											return
							UpperCamelCase_:							Optional[int]		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

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

							return (out_vocab_file,)






