





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Optional[Any]											=	os.path.join(args.tf_model_dir   , 'parameters.json'   )
	snake_case_		:			Dict											=	json.loads(open(__a   ).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'   ):
		snake_case_		:			Dict											=	args.output + '.pt'
	snake_case_		:			Dict											=	OrderedDict()
	with tf.device('/CPU:0'   ):
		snake_case_		:			Union[str, Any]											=	tf.train.load_checkpoint(args.tf_model_dir   )
		snake_case_		:			List[Any]											=	reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			snake_case_		:			Optional[Any]											=	reader.get_tensor(__a   ).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'   ):
					snake_case_		:			str											=	int(key_name[9]   )
				elif key_name.startswith('pasts/out'   ):
					snake_case_		:			Optional[Any]											=	8
				snake_case_		:			Union[str, Any]											=	'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				snake_case_		:			Dict											=	vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
				snake_case_		:			List[str]											=	torch.tensor(__a   )
			elif key_name.startswith('model/moe'   ):
				snake_case_		:			Optional[Any]											=	int(key_name[9:].split('/'   )[0]   )
				if key_name.endswith('/switch_gating/kernel'   ):
					snake_case_		:			List[Any]											=	'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
					snake_case_		:			List[str]											=	vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
					snake_case_		:			Tuple											=	torch.tensor(__a   )
				elif key_name.endswith('/softmlp/kernel'   ):
					snake_case_		:			str											=	'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
					snake_case_		:			Union[str, Any]											=	vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
					snake_case_		:			Dict											=	torch.tensor(__a   )
				elif key_name.endswith('/wo/kernel'   ) or key_name.endswith('/wi/kernel'   ):
					snake_case_		:			Optional[int]											=	key_name[-9:-7]
					for i in range(16   ):
						snake_case_		:			Dict											=	'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
						snake_case_		:			int											=	(
						    vnp[i].transpose([1, 0]   ).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						snake_case_		:			Any											=	torch.tensor(__a   )
			elif key_name.startswith('model/mlp'   ):
				snake_case_		:			Tuple											=	int(key_name[9:].split('/'   )[0]   )
				if key_name.endswith('/p1/kernel'   ):
					snake_case_		:			Optional[int]											=	'model.blocks.%d.feed_forward.mlp.wi.weight' % player
					snake_case_		:			Dict											=	vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
					snake_case_		:			Union[str, Any]											=	torch.tensor(__a   )
				elif key_name.endswith('/p1/bias'   ):
					snake_case_		:			List[str]											=	'model.blocks.%d.feed_forward.mlp.wi.bias' % player
					snake_case_		:			Tuple											=	vnp.copy()  # same because it is one dimensional
					snake_case_		:			Dict											=	torch.tensor(__a   )
				elif key_name.endswith('/p2/kernel'   ):
					snake_case_		:			Optional[Any]											=	'model.blocks.%d.feed_forward.mlp.wo.weight' % player
					snake_case_		:			int											=	vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
					snake_case_		:			Optional[int]											=	torch.tensor(__a   )
				elif key_name.endswith('/p2/bias'   ):
					snake_case_		:			Tuple											=	'model.blocks.%d.feed_forward.mlp.wo.bias' % player
					snake_case_		:			Tuple											=	vnp.copy()  # same because it is one dimensional
					snake_case_		:			Optional[Any]											=	torch.tensor(__a   )
			elif key_name.startswith('model/ln'   ):
				snake_case_		:			Tuple											=	int(key_name[8:].split('/'   )[0]   )
				if key_name.endswith('/b'   ):
					snake_case_		:			Union[str, Any]											=	'model.blocks.%d.feed_forward.norm.bias' % player
					snake_case_		:			Tuple											=	vnp.copy()  # same because it is one dimensional
					snake_case_		:			Tuple											=	torch.tensor(__a   )
				elif key_name.endswith('/g'   ):
					snake_case_		:			str											=	'model.blocks.%d.feed_forward.norm.weight' % player
					snake_case_		:			List[Any]											=	vnp.copy()  # same because it is one dimensional
					snake_case_		:			Optional[int]											=	torch.tensor(__a   )
			elif key_name.startswith('model/att'   ):
				snake_case_		:			Optional[int]											=	int(key_name[9:].split('/'   )[0]   )
				if key_name.endswith('/qkv/kernel'   ):
					snake_case_		:			str											=	vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					snake_case_		:			Dict											=	state[:, 0, :, :]
					snake_case_		:			Optional[int]											=	state[:, 1, :, :]
					snake_case_		:			int											=	state[:, 2, :, :]
					snake_case_		:			int											=	(
					    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
					snake_case_		:			Union[str, Any]											=	(
					    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
					snake_case_		:			str											=	(
					    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
					snake_case_		:			Dict											=	'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
					snake_case_		:			Tuple											=	torch.tensor(__a   )
					snake_case_		:			int											=	'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
					snake_case_		:			int											=	torch.tensor(__a   )
					snake_case_		:			Tuple											=	'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
					snake_case_		:			List[str]											=	torch.tensor(__a   )
				elif key_name.endswith('/o/kernel'   ):
					snake_case_		:			List[Any]											=	'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
					snake_case_		:			Union[str, Any]											=	(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]   ).transpose([1, 0]   ).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					snake_case_		:			Optional[Any]											=	torch.tensor(__a   )
			elif key_name.startswith('model/an'   ):
				snake_case_		:			List[str]											=	int(key_name[8:].split('/'   )[0]   )
				if key_name.endswith('/b'   ):
					snake_case_		:			Union[str, Any]											=	'model.blocks.%d.self_attn.norm.bias' % player
					snake_case_		:			List[Any]											=	vnp.copy()  # same because it is one dimensional
					snake_case_		:			List[str]											=	torch.tensor(__a   )
				elif key_name.endswith('/g'   ):
					snake_case_		:			Optional[int]											=	'model.blocks.%d.self_attn.norm.weight' % player
					snake_case_		:			Any											=	vnp.copy()  # same because it is one dimensional
					snake_case_		:			List[Any]											=	torch.tensor(__a   )
			elif (
			    key_name.startswith('model/wte'   )
			    or key_name.startswith('model/wpe'   )
			    or key_name.startswith('model/ete'   )
			):
				snake_case_		:			int											=	{'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
				    key_name[-3:]
				]
				snake_case_		:			Optional[Any]											=	'model.%s.weight' % nlayer
				snake_case_		:			Dict											=	vnp.copy()  # same in embedded
				snake_case_		:			Optional[int]											=	torch.tensor(__a   )
				if key_name.startswith('model/wte'   ):
					snake_case_		:			Optional[int]											=	'lm_head.weight'
					snake_case_		:			List[str]											=	vnp.copy()  # same in embedded
					snake_case_		:			Any											=	torch.tensor(__a   )
			elif key_name.startswith('model/wob'   ):
				snake_case_		:			Any											=	'final_logits_bias'
				snake_case_		:			Optional[int]											=	vnp.copy()  # same in embedded
				snake_case_		:			Tuple											=	state.reshape((1, -1)   )
				snake_case_		:			Tuple											=	torch.tensor(__a   )
			elif key_name == "model/dense/kernel":
				snake_case_		:			Dict											=	'model.last_project.weight'
				snake_case_		:			Optional[Any]											=	vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
				snake_case_		:			Union[str, Any]											=	torch.tensor(__a   )
			elif key_name == "model/dense_1/bias":
				snake_case_		:			List[str]											=	'model.last_project.bias'
				snake_case_		:			Dict											=	vnp.copy()  # same because it is one dimensional
				snake_case_		:			List[Any]											=	torch.tensor(__a   )
	torch.save(__a   , args.output   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE					=		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""")
						_SCREAMING_SNAKE_CASE					=		parser.parse_args()
						convert_tf_gptsan_to_pt(args)








import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece.model""")
_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_SCREAMING_SNAKE_CASE      	=		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			str       =       CamembertTokenizer
						__magic_name__:			Optional[Any]       =       CamembertTokenizerFast
						__magic_name__:			Optional[int]       =       True
						__magic_name__:			str       =       True






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:			Union[str, Any]											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)






						def UpperCAmelCase_				(      self      :      int	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			int											=	'<pad>'
							snake_case_		:			int											=	1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A	)		,   _A	)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A	)		,   _A	)






						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	list(self.get_tokenizer().get_vocab().keys()	)

							self.assertEqual(vocab_keys[0]		,   '<s>NOTUSED'	)
							self.assertEqual(vocab_keys[1]		,   '<pad>'	)
							self.assertEqual(vocab_keys[-1]		,   '<mask>'	)
							self.assertEqual(len(_A	)		,   1004	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size		,   1005	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Any											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)
							snake_case_		:			List[Any]											=	CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

							snake_case_		:			str											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Dict											=	tokenizer.encode(_A	)
							snake_case_		:			Tuple											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			List[Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			Any											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							snake_case_		:			Union[str, Any]											=	tokenizer.convert_ids_to_tokens(_A	)
							snake_case_		:			Optional[Any]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							if not self.test_rust_tokenizer:
								return

							snake_case_		:			Union[str, Any]											=	self.get_tokenizer()
							snake_case_		:			Any											=	self.get_rust_tokenizer()

							snake_case_		:			Union[str, Any]											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Optional[int]											=	tokenizer.tokenize(_A	)
							snake_case_		:			Dict											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			str											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[int]											=	self.get_rust_tokenizer()
							snake_case_		:			int											=	tokenizer.encode(_A	)
							snake_case_		:			int											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)






						@slow
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							snake_case_		:			Dict											=	[
							    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
							    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
							    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
							    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
							    'telles que la traduction et la synthèse de texte.',
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=_A		,   model_name='camembert-base'		,   revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'		,   sequences=_A		,   )

