





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 io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_SCREAMING_SNAKE_CASE      	=		get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
						_SCREAMING_SNAKE_CASE      	=		json.load(f)
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






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







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






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







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							if torch_device == "cuda":
								model.half()
							return model






						@parameterized.expand(
						    [
						        ['en-ru', 2_6.0],
						        ['ru-en', 2_2.0],
						        ['en-de', 2_2.0],
						        ['de-en', 2_9.0],
						    ]	)
						@slow
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

							snake_case_		:			Optional[int]											=	tokenizer(_A		,   return_tensors='pt'		,   truncation=_A		,   padding='longest'	).to(_A	)
							snake_case_		:			List[Any]											=	model.generate(
							    input_ids=batch.input_ids		,   num_beams=8		,   )
							snake_case_		:			Dict											=	tokenizer.batch_decode(
							    _A		,   skip_special_tokens=_A		,   clean_up_tokenization_spaces=_A	)
							snake_case_		:			Tuple											=	calculate_bleu(_A		,   _A	)
							print(_A	)
							self.assertGreaterEqual(scores['bleu']		,   _A	)

