
"""simple docstring"""




from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


UpperCAmelCase__				:   List[Any]   							=			logging.get_logger(__name__)

UpperCAmelCase__				:   Union[str, Any]   							=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__				:   Union[str, Any]   							=			{
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase__				:   str   							=			{
    'openai/clip-vit-base-patch32': 7_7,
}



class      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase				:						Optional[Any] 	=       VOCAB_FILES_NAMES
				__UpperCamelCase				:						int 	=       PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase				:						Optional[int] 	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase				:						Tuple 	=       ['''input_ids''', '''attention_mask''']
				__UpperCamelCase				:						Optional[Any] 	=       CLIPTokenizer



				def __init__(self       ,	SCREAMING_SNAKE_CASE__=None       ,	SCREAMING_SNAKE_CASE__=None       ,	SCREAMING_SNAKE_CASE__=None       ,	SCREAMING_SNAKE_CASE__="<|endoftext|>"       ,	SCREAMING_SNAKE_CASE__="<|startoftext|>"       ,	SCREAMING_SNAKE_CASE__="<|endoftext|>"       ,	SCREAMING_SNAKE_CASE__="<|endoftext|>"       ,	**SCREAMING_SNAKE_CASE__       ,	) ->		str:



											"""simple docstring"""




											super().__init__(
											    SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	tokenizer_file=SCREAMING_SNAKE_CASE__       ,	unk_token=SCREAMING_SNAKE_CASE__       ,	bos_token=SCREAMING_SNAKE_CASE__       ,	eos_token=SCREAMING_SNAKE_CASE__       ,	pad_token=SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__       ,	)

											if not isinstance(self.backend_tokenizer.pre_tokenizer       ,	pre_tokenizers.Sequence   ):
																		raise ValueError(
																		    """The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"""
																		    """ heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"""
																		    """ to be compatible with this version.The easiest way to do so is"""
																		    """ `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"""
																		    """ to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"""
																		    """ transformers."""   )

											self._wrap_decode_method_backend_tokenizer()



				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.backend_tokenizer.decode

											def new_decode_method(*SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       orig_decode_method(*SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	int									=       text.replace(self.backend_tokenizer.model.end_of_word_suffix       ,	""" """   ).strip()
																		return text

											SCREAMING_SNAKE_CASE__						:	int									=       new_decode_method



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		List[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       [self.bos_token_id]
											SCREAMING_SNAKE_CASE__						:	Tuple									=       [self.eos_token_id]

											if token_ids_a is None:
																		return bos_token + token_ids_a + eos_token
											return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		List[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       [self.bos_token_id]
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       [self.eos_token_id]

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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		Tuple[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       self._tokenizer.model.save(SCREAMING_SNAKE_CASE__       ,	name=SCREAMING_SNAKE_CASE__   )
											return tuple(SCREAMING_SNAKE_CASE__   )






"""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 lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       FNetConfig.from_json_file(_snake_case						)
							print(f'''Building PyTorch model from configuration: {config}'''						)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       FNetForPreTraining(_snake_case						)

							SCREAMING_SNAKE_CASE__						:	Tuple									=       restore_checkpoint(_snake_case       ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       checkpoint_dict["""target"""]

							# Embeddings
							# Position IDs
							SCREAMING_SNAKE_CASE__						:	Tuple									=       fnet_pretraining_model.state_dict()

							SCREAMING_SNAKE_CASE__						:	Any									=       state_dict["""fnet.embeddings.position_ids"""]
							SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""fnet.embeddings.position_ids""": position_ids}
							# Embedding Layers
							SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Any									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]						)

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

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]						)

							# Pooler Layers
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]						)

							# Masked LM Layers
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_output"""]["""output_bias"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]						)

							# Seq Relationship Layers
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_kernel"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       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__":
					UpperCAmelCase__				:   Optional[int]   							=			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.')
					UpperCAmelCase__				:   Optional[Any]   							=			parser.parse_args()
					convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




