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


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



'''simple docstring'''







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A=5			) ->			Dict:
	# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
	assert masked_input.count('<mask>'			) == 1
	_snake_case     		=	torch.tensor(tokenizer.encode(__A    ,       add_special_tokens=__A			)			).unsqueeze(0			)  # Batch size 1
	_snake_case     		=	model(__A			)[0]  # The last hidden-state is the first element of the output tuple
	_snake_case     		=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	_snake_case     		=	logits[0, masked_index, :]
	_snake_case     		=	logits.softmax(dim=0			)
	_snake_case		,				_snake_case     		=	prob.topk(k=__A    ,       dim=0			)
	_snake_case     		=	' '.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()			) for i in range(len(__A			)			)]			)
	_snake_case     		=	tokenizer.mask_token
	_snake_case     		=	[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '			)			):
		_snake_case     		=	predicted_token_bpe.replace('\u2581'    ,       ' '			)
		if " {0}".format(__A			) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(' {0}'.format(__A			)    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(__A    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
	return topk_filled_outputs


lowercase					:							str   =      CamembertTokenizer.from_pretrained("camembert-base")
lowercase					:							str   =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowercase					:							Tuple   =      "Le camembert est <mask> :)"





print(fill_mask(masked_input, model, tokenizer, topk=3))

