
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



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


if __name__ == "__main__":
							lowercase__	:List[Any]										=					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__	:int										=					parser.parse_args()
							convert_tf_gptsan_to_pt(args)






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							lowercase__	:Optional[Any]										=					"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					if rng is None:
										lowercase					=		random.Random()

					lowercase					=		1
					for dim in shape:
										total_dims *= dim

					lowercase					=		[]
					for _ in range(lowerCAmelCase__	):
										values.append(rng.randint(0				,    vocab_size - 1	)	)

					lowercase					=		np.array(lowerCAmelCase__				,    dtype=jnp.intaa	).reshape(lowerCAmelCase__	)

					return output



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					lowercase					=		ids_tensor(lowerCAmelCase__				,    vocab_size=2				,    rng=lowerCAmelCase__	)
					# make sure that at least one token is attended to for each batch
					lowercase					=		1
					return attn_mask







@require_flax
class        lowercase	:
							lowercase_ : Any       =None
							lowercase_ : List[str]       =()




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

												# cut to half length & take max batch_size 3
												lowercase					=		2
												lowercase					=		inputs['''input_ids'''].shape[-1] // 2
												lowercase					=		inputs['''input_ids'''][:max_batch_size, :sequence_length]

												lowercase					=		jnp.ones_like(A__)
												lowercase					=		attention_mask[:max_batch_size, :sequence_length]

												# generate max 5 tokens
												lowercase					=		input_ids.shape[-1] + 5
												if config.eos_token_id is not None and config.pad_token_id is None:
																	# hack to allow generate for models such as GPT2 as is done in `generate()`
																	lowercase					=		config.eos_token_id
												return config, input_ids, attention_mask, max_length




							@is_pt_flax_cross_test
							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		0

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	lowercase					=		getattr(A__     ,A__)
																	lowercase					=		pt_model_class(A__).eval()
																	lowercase					=		load_flax_weights_in_pytorch_model(A__     ,flax_model.params)

																	lowercase					=		flax_model.generate(A__).sequences
																	lowercase					=		pt_model.generate(torch.tensor(A__     ,dtype=torch.long))

																	if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						lowercase					=		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																	self.assertListEqual(pt_generation_outputs.numpy().tolist()     ,flax_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[0]     ,input_ids.shape[0] * config.num_return_sequences)




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length
												lowercase					=		0.8
												lowercase					=		1_0
												lowercase					=		0.3
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())





							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		2
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())







@require_flax
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												lowercase					=		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''')
												lowercase					=		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')

												lowercase					=		'''Hello world'''
												lowercase					=		tokenizer(A__     ,return_tensors='''np''').input_ids

												# typos are quickly detected (the correct argument is `do_sample`)
												with self.assertRaisesRegex(A__     ,'''do_samples'''):
																	model.generate(A__     ,do_samples=A__)

												# arbitrary arguments that will not be used anywhere are also not accepted
												with self.assertRaisesRegex(A__     ,'''foo'''):
																	lowercase					=		{'''foo''': '''bar'''}
																	model.generate(A__     ,**A__)




