





"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertConfig,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertWithLMHeadModel,
		)


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__      ,					)	-> Optional[int]:
								a      :		int	=				parent
								a      :		Any	=				13
								a      :		Optional[Any]	=				7
								a      :		Optional[int]	=				True
								a      :		Optional[int]	=				True
								a      :		List[Any]	=				True
								a      :		Union[str, Any]	=				True
								a      :		List[Any]	=				True
								a      :		Optional[int]	=				False
								a      :		Optional[int]	=				False
								a      :		List[Any]	=				False
								a      :		int	=				2
								a      :		Optional[Any]	=				99
								a      :		Dict	=				0
								a      :		Optional[int]	=				32
								a      :		Tuple	=				2
								a      :		Union[str, Any]	=				4
								a      :		List[str]	=				0.1
								a      :		str	=				0.1
								a      :		List[Any]	=				512
								a      :		Dict	=				16
								a      :		int	=				2
								a      :		List[str]	=				0.02
								a      :		str	=				3
								a      :		List[Any]	=				4
								a      :		Any	=				"last"
								a      :		Union[str, Any]	=				True
								a      :		Any	=				None
								a      :		Dict	=				0






							def     __a   (   self      )	-> Optional[int]:
								a      :		List[str]	=				ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size      )
								a      :		int	=				random_attention_mask([self.batch_size, self.seq_length]      ,					dtype=tf.floataa      )

								a      :		int	=				None
								if self.use_input_lengths:
									a      :		str	=				(
									    ids_tensor([self.batch_size]      ,					vocab_size=2      ) + self.seq_length - 2
									)  # small variation of seq_length

								a      :		str	=				None
								if self.use_token_type_ids:
									a      :		str	=				ids_tensor([self.batch_size, self.seq_length]      ,					self.n_langs      )

								a      :		Optional[int]	=				None
								a      :		Optional[Any]	=				None
								a      :		Tuple	=				None
								if self.use_labels:
									a      :		Union[str, Any]	=				ids_tensor([self.batch_size]      ,					self.type_sequence_label_size      )
									a      :		List[str]	=				ids_tensor([self.batch_size, self.seq_length]      ,					self.num_labels      )
									a      :		List[Any]	=				ids_tensor([self.batch_size]      ,					2      ,					dtype=tf.floataa      )
									a      :		Optional[int]	=				ids_tensor([self.batch_size]      ,					self.num_choices      )

								a      :		List[str]	=				FlaubertConfig(
								    vocab_size=self.vocab_size      ,					n_special=self.n_special      ,					emb_dim=self.hidden_size      ,					n_layers=self.num_hidden_layers      ,					n_heads=self.num_attention_heads      ,					dropout=self.hidden_dropout_prob      ,					attention_dropout=self.attention_probs_dropout_prob      ,					gelu_activation=self.gelu_activation      ,					sinusoidal_embeddings=self.sinusoidal_embeddings      ,					asm=self.asm      ,					causal=self.causal      ,					n_langs=self.n_langs      ,					max_position_embeddings=self.max_position_embeddings      ,					initializer_range=self.initializer_range      ,					summary_type=self.summary_type      ,					use_proj=self.use_proj      ,					bos_token_id=self.bos_token_id      ,					)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_lengths,
								    sequence_labels,
								    token_labels,
								    is_impossible_labels,
								    choice_labels,
								    input_mask,
								)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Tuple:
								a      :		Tuple	=				TFFlaubertModel(config=lowerCAmelCase__      )
								a      :		List[str]	=				{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
								a      :		Dict	=				model(lowerCAmelCase__      )

								a      :		List[Any]	=				[input_ids, input_mask]
								a      :		List[str]	=				model(lowerCAmelCase__      )
								self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Union[str, Any]:
								a      :		Dict	=				TFFlaubertWithLMHeadModel(lowerCAmelCase__      )

								a      :		Optional[Any]	=				{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
								a      :		Dict	=				model(lowerCAmelCase__      )

								self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size)      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Dict:
								a      :		Union[str, Any]	=				TFFlaubertForQuestionAnsweringSimple(lowerCAmelCase__      )

								a      :		Union[str, Any]	=				{"input_ids": input_ids, "lengths": input_lengths}

								a      :		Optional[int]	=				model(lowerCAmelCase__      )

								self.parent.assertEqual(result.start_logits.shape      ,					(self.batch_size, self.seq_length)      )
								self.parent.assertEqual(result.end_logits.shape      ,					(self.batch_size, self.seq_length)      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> int:
								a      :		str	=				TFFlaubertForSequenceClassification(lowerCAmelCase__      )

								a      :		Dict	=				{"input_ids": input_ids, "lengths": input_lengths}

								a      :		List[str]	=				model(lowerCAmelCase__      )

								self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.type_sequence_label_size)      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> int:
								a      :		int	=				self.num_labels
								a      :		Tuple	=				TFFlaubertForTokenClassification(config=lowerCAmelCase__      )
								a      :		Any	=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
								a      :		List[str]	=				model(lowerCAmelCase__      )
								self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.num_labels)      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> str:
								a      :		Dict	=				self.num_choices
								a      :		Optional[Any]	=				TFFlaubertForMultipleChoice(config=lowerCAmelCase__      )
								a      :		int	=				tf.tile(tf.expand_dims(lowerCAmelCase__      ,					1      )      ,					(1, self.num_choices, 1)      )
								a      :		List[str]	=				tf.tile(tf.expand_dims(lowerCAmelCase__      ,					1      )      ,					(1, self.num_choices, 1)      )
								a      :		Optional[int]	=				tf.tile(tf.expand_dims(lowerCAmelCase__      ,					1      )      ,					(1, self.num_choices, 1)      )
								a      :		Tuple	=				{
								    "input_ids": multiple_choice_inputs_ids,
								    "attention_mask": multiple_choice_input_mask,
								    "token_type_ids": multiple_choice_token_type_ids,
								}
								a      :		List[Any]	=				model(lowerCAmelCase__      )
								self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_choices)      )






							def     __a   (   self      )	-> Dict:
								a      :		List[Any]	=				self.prepare_config_and_inputs()
								(
								    (
								    a
								),       (
								    a
								),       (
								    a
								),       (
								    a
								),       (
								    a
								),       (
								    a
								),       (
								    a
								),       (
								    a
								),       (
								    a
								),       
								)      :		Dict	=				config_and_inputs
								a      :		int	=				{
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								    "langs": token_type_ids,
								    "lengths": input_lengths,
								}
								return config, inputs_dict


@require_tf
class        __UpperCamelCase				(						a__		,       a__		,       unittest.TestCase						):
							lowerCamelCase   :					int      					=(
							    (
							        TFFlaubertModel,
							        TFFlaubertWithLMHeadModel,
							        TFFlaubertForSequenceClassification,
							        TFFlaubertForQuestionAnsweringSimple,
							        TFFlaubertForTokenClassification,
							        TFFlaubertForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							lowerCamelCase   :					Any      					=(
							    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
							)  # TODO (PVP): Check other models whether language generation is also applicable
							lowerCamelCase   :					List[str]      					=(
							    {
							        """feature-extraction""": TFFlaubertModel,
							        """fill-mask""": TFFlaubertWithLMHeadModel,
							        """question-answering""": TFFlaubertForQuestionAnsweringSimple,
							        """text-classification""": TFFlaubertForSequenceClassification,
							        """token-classification""": TFFlaubertForTokenClassification,
							        """zero-shot""": TFFlaubertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase   :					List[str]      					=False
							lowerCamelCase   :					int      					=False






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Tuple:
								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith("Fast"      )
								):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

								return False






							def     __a   (   self      )	-> Union[str, Any]:
								a      :		Optional[Any]	=				TFFlaubertModelTester(self      )
								a      :		Any	=				ConfigTester(self      ,					config_class=lowerCAmelCase__      ,					emb_dim=37      )






							def     __a   (   self      )	-> Union[str, Any]:
								self.config_tester.run_common_tests()






							def     __a   (   self      )	-> Tuple:
								a      :		Optional[int]	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_model(*lowerCAmelCase__      )






							def     __a   (   self      )	-> Union[str, Any]:
								a      :		List[str]	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_lm_head(*lowerCAmelCase__      )






							def     __a   (   self      )	-> int:
								a      :		Union[str, Any]	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_qa(*lowerCAmelCase__      )






							def     __a   (   self      )	-> Union[str, Any]:
								a      :		Dict	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_sequence_classif(*lowerCAmelCase__      )






							def     __a   (   self      )	-> Optional[Any]:
								a      :		str	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_token_classification(*lowerCAmelCase__      )






							def     __a   (   self      )	-> Any:
								a      :		Dict	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowerCAmelCase__      )






							@slow
							def     __a   (   self      )	-> Optional[Any]:
								for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a      :		Any	=				TFFlaubertModel.from_pretrained(lowerCAmelCase__      )
									self.assertIsNotNone(lowerCAmelCase__      )


@require_tf
@require_sentencepiece
@require_tokenizers
class        __UpperCamelCase				(						unittest.TestCase						):






							@slow
							def     __a   (   self      )	-> Any:
								a      :		Union[str, Any]	=				TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased"      )

								a      :		Optional[int]	=				tf.convert_to_tensor(
								    [[0, 158, 735, 2592, 1424, 6727, 82, 1]]      ,					dtype=tf.intaa      ,					)  # "J'aime flaubert !"

								a      :		List[str]	=				model(lowerCAmelCase__      )[0]
								a      :		str	=				tf.TensorShape((1, 8, 512)      )
								self.assertEqual(output.shape      ,					lowerCAmelCase__      )
								# compare the actual values for a slice.
								a      :		Union[str, Any]	=				tf.convert_to_tensor(
								    [
								        [
								            [-1.8_768_773, -1.566_555, 0.27_072_418],
								            [-1.6_920_038, -0.5_873_505, 1.9_329_599],
								            [-2.9_563_985, -1.6_993_835, 1.7_972_052],
								        ]
								    ]      ,					dtype=tf.floataa      ,					)

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,					expected_slice.numpy()      ,					atol=1E-4      )      )









"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]					) ->str:






	'''simple docstring'''






	a      :		Union[str, Any]	=				os.path.join(args.tf_model_dir    ,       "parameters.json"					)
	a      :		str	=				json.loads(open(_lowercase					).read()					)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""					)
	if not args.output.endswith(".pt"					):
		a      :		str	=				args.output + ".pt"
	a      :		Dict	=				OrderedDict()
	with tf.device("/CPU:0"					):
		a      :		Optional[int]	=				tf.train.load_checkpoint(args.tf_model_dir					)
		a      :		Optional[Any]	=				reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			a      :		Dict	=				reader.get_tensor(_lowercase					).astype(np.floataa					)
			if key_name.endswith("/adam_m"					) or key_name.endswith("/adam_v"					):
				continue
			if key_name.startswith("pasts/"					):
				if key_name.startswith("pasts/mlp"					):
					a      :		Union[str, Any]	=				int(key_name[9]					)
				elif key_name.startswith("pasts/out"					):
					a      :		Optional[int]	=				8
				a      :		Dict	=				"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				a      :		int	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/moe"					):
				a      :		List[str]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/switch_gating/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/softmlp/kernel"					):
					a      :		Optional[int]	=				"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/wo/kernel"					) or key_name.endswith("/wi/kernel"					):
					a      :		Any	=				key_name[-9:-7]
					for i in range(16					):
						a      :		List[Any]	=				"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
						a      :		str	=				(
						    vnp[i].transpose([1, 0]					).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/mlp"					):
				a      :		Union[str, Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/p1/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.weight" % player
					a      :		Dict	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p1/bias"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.bias" % player
					a      :		List[Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/kernel"					):
					a      :		Union[str, Any]	=				"model.blocks.%d.feed_forward.mlp.wo.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/bias"					):
					a      :		Dict	=				"model.blocks.%d.feed_forward.mlp.wo.bias" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		str	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/ln"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.bias" % player
					a      :		Tuple	=				vnp.copy()  # same because it is one dimensional
					a      :		int	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.weight" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/att"					):
				a      :		Optional[Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/qkv/kernel"					):
					a      :		Union[str, Any]	=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					a      :		List[str]	=				state[:, 0, :, :]
					a      :		Dict	=				state[:, 1, :, :]
					a      :		Union[str, Any]	=				state[:, 2, :, :]
					a      :		str	=				(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Dict	=				(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
					a      :		Union[str, Any]	=				torch.tensor(_lowercase					)
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					a      :		List[str]	=				torch.tensor(_lowercase					)
					a      :		Optional[Any]	=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					a      :		Optional[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/o/kernel"					):
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
					a      :		Optional[int]	=				(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/an"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[int]	=				"model.blocks.%d.self_attn.norm.bias" % player
					a      :		Union[str, Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Any	=				"model.blocks.%d.self_attn.norm.weight" % player
					a      :		str	=				vnp.copy()  # same because it is one dimensional
					a      :		Any	=				torch.tensor(_lowercase					)
			elif (
			    key_name.startswith("model/wte"					)
			    or key_name.startswith("model/wpe"					)
			    or key_name.startswith("model/ete"					)
			):
				a      :		Optional[int]	=				{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
				    key_name[-3:]
				]
				a      :		Tuple	=				"model.%s.weight" % nlayer
				a      :		Any	=				vnp.copy()  # same in embedded
				a      :		Tuple	=				torch.tensor(_lowercase					)
				if key_name.startswith("model/wte"					):
					a      :		Optional[int]	=				"lm_head.weight"
					a      :		Optional[int]	=				vnp.copy()  # same in embedded
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/wob"					):
				a      :		Optional[int]	=				"final_logits_bias"
				a      :		Optional[Any]	=				vnp.copy()  # same in embedded
				a      :		Optional[int]	=				state.reshape((1, -1)					)
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense/kernel":
				a      :		Optional[int]	=				"model.last_project.weight"
				a      :		Union[str, Any]	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense_1/bias":
				a      :		Dict	=				"model.last_project.bias"
				a      :		Optional[Any]	=				vnp.copy()  # same because it is one dimensional
				a      :		Any	=				torch.tensor(_lowercase					)
	torch.save(_lowercase    ,       args.output					)


if __name__ == "__main__":
		a						: Optional[int]     	=			argparse.ArgumentParser(
		    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
		parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
		a						: Tuple     	=			parser.parse_args()
		convert_tf_gptsan_to_pt(args)


