
"""simple docstring"""




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class      lowerCAmelCase_						(a__			, unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase				:						str 	=       BartTokenizer
				__UpperCamelCase				:						List[str] 	=       BartTokenizerFast
				__UpperCamelCase				:						Optional[int] 	=       True
				__UpperCamelCase				:						Dict 	=       filter_roberta_detectors



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



											"""simple docstring"""




											super().setUp()
											SCREAMING_SNAKE_CASE__						:	str									=       [
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """\u0120""",
											    """\u0120l""",
											    """\u0120n""",
											    """\u0120lo""",
											    """\u0120low""",
											    """er""",
											    """\u0120lowest""",
											    """\u0120newer""",
											    """\u0120wider""",
											    """<unk>""",
											]
											SCREAMING_SNAKE_CASE__						:	Tuple									=       dict(zip(SCREAMING_SNAKE_CASE__       ,	range(len(SCREAMING_SNAKE_CASE__   )   )   )   )
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
											SCREAMING_SNAKE_CASE__						:	Dict									=       {"""unk_token""": """<unk>"""}

											SCREAMING_SNAKE_CASE__						:	str									=       os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["""vocab_file"""]   )
											SCREAMING_SNAKE_CASE__						:	Tuple									=       os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["""merges_file"""]   )
											with open(self.vocab_file       ,	"""w"""       ,	encoding="""utf-8"""   ) as fp:
																		fp.write(json.dumps(SCREAMING_SNAKE_CASE__   ) + """\n"""   )
											with open(self.merges_file       ,	"""w"""       ,	encoding="""utf-8"""   ) as fp:
																		fp.write("""\n""".join(SCREAMING_SNAKE_CASE__   )   )



				def      __magic_name__   (self       ,	**SCREAMING_SNAKE_CASE__   ) ->		List[Any]:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map   )
											return self.tokenizer_class.from_pretrained(self.tmpdirname       ,	**SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	**SCREAMING_SNAKE_CASE__   ) ->		List[Any]:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map   )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,	**SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Optional[Any]:



											"""simple docstring"""




											return "lower newer", "lower newer"



				@cached_property
				def      __magic_name__   (self   ) ->		List[str]:



											"""simple docstring"""




											return BartTokenizer.from_pretrained("""facebook/bart-large"""   )



				@cached_property
				def      __magic_name__   (self   ) ->		Union[str, Any]:



											"""simple docstring"""




											return BartTokenizerFast.from_pretrained("""facebook/bart-large"""   )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											SCREAMING_SNAKE_CASE__						:	str									=       [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:	str									=       tokenizer(SCREAMING_SNAKE_CASE__       ,	max_length=len(SCREAMING_SNAKE_CASE__   )       ,	padding=SCREAMING_SNAKE_CASE__       ,	return_tensors="""pt"""   )
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )

																		self.assertEqual((2, 9)       ,	batch.input_ids.shape   )
																		self.assertEqual((2, 9)       ,	batch.attention_mask.shape   )
																		SCREAMING_SNAKE_CASE__						:	str									=       batch.input_ids.tolist()[0]
																		self.assertListEqual(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
																		# Test that special tokens are reset



				@require_torch
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[str]									=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       tokenizer(SCREAMING_SNAKE_CASE__       ,	padding=SCREAMING_SNAKE_CASE__       ,	return_tensors="""pt"""   )
																		# check if input_ids are returned and no labels
																		self.assertIn("""input_ids"""       ,	SCREAMING_SNAKE_CASE__   )
																		self.assertIn("""attention_mask"""       ,	SCREAMING_SNAKE_CASE__   )
																		self.assertNotIn("""labels"""       ,	SCREAMING_SNAKE_CASE__   )
																		self.assertNotIn("""decoder_attention_mask"""       ,	SCREAMING_SNAKE_CASE__   )



				@require_torch
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       [
											    """Summary of the text.""",
											    """Another summary.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       tokenizer(text_target=SCREAMING_SNAKE_CASE__       ,	max_length=32       ,	padding="""max_length"""       ,	return_tensors="""pt"""   )
																		self.assertEqual(32       ,	targets["""input_ids"""].shape[1]   )



				@require_torch
				def      __magic_name__   (self   ) ->		List[str]:



											"""simple docstring"""




											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       tokenizer(
																		    ["""I am a small frog""" * 10_24, """I am a small frog"""]       ,	padding=SCREAMING_SNAKE_CASE__       ,	truncation=SCREAMING_SNAKE_CASE__       ,	return_tensors="""pt"""   )
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
																		self.assertEqual(batch.input_ids.shape       ,	(2, 10_24)   )



				@require_torch
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       ["""A long paragraph for summarization."""]
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       [
											    """Summary of the text.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       tokenizer(SCREAMING_SNAKE_CASE__       ,	return_tensors="""pt"""   )
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       tokenizer(text_target=SCREAMING_SNAKE_CASE__       ,	return_tensors="""pt"""   )
																		SCREAMING_SNAKE_CASE__						:	Dict									=       inputs["""input_ids"""]
																		SCREAMING_SNAKE_CASE__						:	Dict									=       targets["""input_ids"""]
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )



				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




											pass



				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__   )
																									SCREAMING_SNAKE_CASE__						:	List[str]									=       self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__   )
																									SCREAMING_SNAKE_CASE__						:	Dict									=       """A, <mask> AllenNLP sentence."""
																									SCREAMING_SNAKE_CASE__						:	Dict									=       tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__       ,	add_special_tokens=SCREAMING_SNAKE_CASE__       ,	return_token_type_ids=SCREAMING_SNAKE_CASE__   )
																									SCREAMING_SNAKE_CASE__						:	List[Any]									=       tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__       ,	add_special_tokens=SCREAMING_SNAKE_CASE__       ,	return_token_type_ids=SCREAMING_SNAKE_CASE__   )

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r["""token_type_ids"""]   )       ,	sum(tokens_p["""token_type_ids"""]   )   )

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									self.assertEqual(
																									    sum(tokens_r["""attention_mask"""]   ) / len(tokens_r["""attention_mask"""]   )       ,	sum(tokens_p["""attention_mask"""]   ) / len(tokens_p["""attention_mask"""]   )       ,	)

																									SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]   )
																									SCREAMING_SNAKE_CASE__						:	Any									=       tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]   )

																									# Rust correctly handles the space before the mask while python doesnt
																									self.assertSequenceEqual(tokens_p["""input_ids"""]       ,	[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
																									self.assertSequenceEqual(tokens_r["""input_ids"""]       ,	[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )

																									self.assertSequenceEqual(
																									    SCREAMING_SNAKE_CASE__       ,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )
																									self.assertSequenceEqual(
																									    SCREAMING_SNAKE_CASE__       ,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )






"""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)




