

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
					import torch

class 				a  :


						def __init__(       self						,					A_						,					A_=14						,					A_=7						,					A_=True						,					A_=True						,					A_=False						,					A_=True						,					A_=99						,					A_=32						,					A_=4						,					A_=4						,					A_=4						,					A_=37						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=0.02						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    parent
										_UpperCAmelCase       :		Optional[int] 		=    batch_size
										_UpperCAmelCase       :		Tuple 		=    seq_length
										_UpperCAmelCase       :		Optional[Any] 		=    is_training
										_UpperCAmelCase       :		str 		=    use_input_mask
										_UpperCAmelCase       :		Optional[Any] 		=    use_token_type_ids
										_UpperCAmelCase       :		str 		=    use_labels
										_UpperCAmelCase       :		Dict 		=    vocab_size
										_UpperCAmelCase       :		str 		=    hidden_size
										_UpperCAmelCase       :		List[Any] 		=    rotary_dim
										_UpperCAmelCase       :		Optional[Any] 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    intermediate_size
										_UpperCAmelCase       :		List[str] 		=    hidden_act
										_UpperCAmelCase       :		Optional[int] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		int 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Tuple 		=    max_position_embeddings
										_UpperCAmelCase       :		Dict 		=    initializer_range
										_UpperCAmelCase       :		Tuple 		=    None
										_UpperCAmelCase       :		Optional[int] 		=    vocab_size - 1
										_UpperCAmelCase       :		Optional[int] 		=    vocab_size - 1
										_UpperCAmelCase       :		Tuple 		=    vocab_size - 1


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size	)

										_UpperCAmelCase       :		List[str] 		=    None
										if self.use_input_mask:
														_UpperCAmelCase       :		str 		=    random_attention_mask([self.batch_size, self.seq_length]	)

										_UpperCAmelCase       :		List[Any] 		=    GPTJConfig(
										    vocab_size=self.vocab_size						,					n_embd=self.hidden_size						,					n_layer=self.num_hidden_layers						,					n_head=self.num_attention_heads						,					n_positions=self.max_position_embeddings						,					use_cache=A_						,					bos_token_id=self.bos_token_id						,					eos_token_id=self.eos_token_id						,					pad_token_id=self.pad_token_id						,					rotary_dim=self.rotary_dim						,					)

										return (config, input_ids, input_mask)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.prepare_config_and_inputs()
										_UpperCAmelCase       :		List[str] 		=    config_and_inputs
										_UpperCAmelCase       :		Union[str, Any] 		=    {"input_ids": input_ids, "attention_mask": attention_mask}
										return config, inputs_dict


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    20
										_UpperCAmelCase       :		Dict 		=    model_class_name(A_	)

										_UpperCAmelCase       :		str 		=    model.init_cache(input_ids.shape[0]						,					A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    jnp.ones((input_ids.shape[0], max_decoder_length)						,					dtype="i4"	)

										_UpperCAmelCase       :		Dict 		=    jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1	)[None, :]						,					(input_ids.shape[0], input_ids.shape[-1] - 1)	)
										_UpperCAmelCase       :		Optional[Any] 		=    model(
										    input_ids[:, :-1]						,					attention_mask=A_						,					past_key_values=A_						,					position_ids=A_						,					)

										_UpperCAmelCase       :		int 		=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,					dtype="i4"	)
										_UpperCAmelCase       :		Optional[Any] 		=    model(
										    input_ids[:, -1:]						,					attention_mask=A_						,					past_key_values=outputs_cache.past_key_values						,					position_ids=A_						,					)

										_UpperCAmelCase       :		Optional[Any] 		=    model(A_	)

										_UpperCAmelCase       :		Any 		=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
										self.parent.assertTrue(diff < 1e-3						,					msg=f'Max diff is {diff}'	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    20
										_UpperCAmelCase       :		Dict 		=    model_class_name(A_	)

										_UpperCAmelCase       :		List[Any] 		=    jnp.concatenate(
										    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]						,					axis=-1						,					)

										_UpperCAmelCase       :		Optional[int] 		=    model.init_cache(input_ids.shape[0]						,					A_	)
										_UpperCAmelCase       :		str 		=    jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1	)[None, :]						,					(input_ids.shape[0], input_ids.shape[-1] - 1)	)

										_UpperCAmelCase       :		Any 		=    model(
										    input_ids[:, :-1]						,					attention_mask=A_						,					past_key_values=A_						,					position_ids=A_						,					)
										_UpperCAmelCase       :		Tuple 		=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,					dtype="i4"	)
										_UpperCAmelCase       :		Dict 		=    model(
										    input_ids[:, -1:]						,					past_key_values=outputs_cache.past_key_values						,					attention_mask=A_						,					position_ids=A_						,					)

										_UpperCAmelCase       :		Any 		=    model(A_						,					attention_mask=A_	)

										_UpperCAmelCase       :		List[Any] 		=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
										self.parent.assertTrue(diff < 1e-3						,					msg=f'Max diff is {diff}'	)







@require_flax
class 				a  (	UpperCAmelCase							, UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
						_lowercase    						=			(FlaxGPTJForCausalLM,) if is_flax_available() else ()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    FlaxGPTJModelTester(self	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										for model_class_name in self.all_model_classes:
														_UpperCAmelCase       :		Optional[Any] 		=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(A_						,					A_						,					A_						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										for model_class_name in self.all_model_classes:
														_UpperCAmelCase       :		str 		=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    A_						,					A_						,					A_						,					A_	)


						@tooslow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    GPTaTokenizer.from_pretrained("gpt2"						,					pad_token="<|endoftext|>"						,					padding_side="left"	)
										_UpperCAmelCase       :		Optional[int] 		=    tokenizer(["Hello this is a long string", "Hey"]						,					return_tensors="np"						,					padding=A_						,					truncation=A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	)
										_UpperCAmelCase       :		Optional[int] 		=    False
										_UpperCAmelCase       :		List[Any] 		=    model.config.eos_token_id

										_UpperCAmelCase       :		Tuple 		=    jax.jit(model.generate	)

										_UpperCAmelCase       :		Any 		=    jit_generate(
										    inputs["input_ids"]						,					attention_mask=inputs["attention_mask"]						,					pad_token_id=tokenizer.pad_token_id	).sequences

										_UpperCAmelCase       :		Tuple 		=    tokenizer.batch_decode(A_						,					skip_special_tokens=A_	)

										_UpperCAmelCase       :		List[Any] 		=    [
										    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
										    "Hey, I'm a little late to the party. I'm going to",
										]

										self.assertListEqual(A_						,					A_	)


						@is_pt_flax_cross_test
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__	):
																		# prepare inputs
																		_UpperCAmelCase       :		Dict 		=    self._prepare_for_class(A_						,					A_	)
																		_UpperCAmelCase       :		Union[str, Any] 		=    {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

																		# load corresponding PyTorch class
																		_UpperCAmelCase       :		Optional[Any] 		=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		_UpperCAmelCase       :		Optional[int] 		=    getattr(A_						,					A_	)

																		_UpperCAmelCase       :		List[Any] 		=    pt_inputs["input_ids"].shape
																		_UpperCAmelCase       :		Tuple 		=    np.random.randint(0						,					seq_length - 1						,					size=(batch_size,)	)
																		for batch_idx, start_index in enumerate(A_	):
																						_UpperCAmelCase       :		int 		=    0
																						_UpperCAmelCase       :		Optional[Any] 		=    1
																						_UpperCAmelCase       :		Union[str, Any] 		=    0
																						_UpperCAmelCase       :		Optional[int] 		=    1
																		_UpperCAmelCase       :		Any 		=    pt_model_class(A_	).eval()
																		_UpperCAmelCase       :		Tuple 		=    model_class(A_						,					dtype=jnp.floataa	)

																		_UpperCAmelCase       :		Dict 		=    convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,					A_	)
																		_UpperCAmelCase       :		Any 		=    fx_state

																		with torch.no_grad():
																						_UpperCAmelCase       :		str 		=    pt_model(**A_	).to_tuple()

																		_UpperCAmelCase       :		List[str] 		=    fx_model(**A_	).to_tuple()
																		self.assertEqual(len(A_	)						,					len(A_	)						,					"Output lengths differ between Flax and PyTorch"	)
																		for fx_output, pt_output in zip(A_						,					A_	):
																						self.assert_almost_equals(fx_output[:, -1]						,					pt_output[:, -1].numpy()						,					4e-2	)

																		with tempfile.TemporaryDirectory() as tmpdirname:
																						pt_model.save_pretrained(A_	)
																						_UpperCAmelCase       :		List[str] 		=    model_class.from_pretrained(A_						,					from_pt=A_	)

																		_UpperCAmelCase       :		Union[str, Any] 		=    fx_model_loaded(**A_	).to_tuple()
																		self.assertEqual(
																		    len(A_	)						,					len(A_	)						,					"Output lengths differ between Flax and PyTorch"	)
																		for fx_output_loaded, pt_output in zip(A_						,					A_	):
																						self.assert_almost_equals(fx_output_loaded[:, -1]						,					pt_output[:, -1].numpy()						,					4e-2	)


						@is_pt_flax_cross_test
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__	):
																		# prepare inputs
																		_UpperCAmelCase       :		Tuple 		=    self._prepare_for_class(A_						,					A_	)
																		_UpperCAmelCase       :		str 		=    {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

																		# load corresponding PyTorch class
																		_UpperCAmelCase       :		Optional[Any] 		=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		_UpperCAmelCase       :		str 		=    getattr(A_						,					A_	)

																		_UpperCAmelCase       :		Optional[Any] 		=    pt_model_class(A_	).eval()
																		_UpperCAmelCase       :		Dict 		=    model_class(A_						,					dtype=jnp.floataa	)

																		_UpperCAmelCase       :		List[str] 		=    load_flax_weights_in_pytorch_model(A_						,					fx_model.params	)
																		_UpperCAmelCase       :		int 		=    pt_inputs["input_ids"].shape
																		_UpperCAmelCase       :		Optional[int] 		=    np.random.randint(0						,					seq_length - 1						,					size=(batch_size,)	)
																		for batch_idx, start_index in enumerate(A_	):
																						_UpperCAmelCase       :		Dict 		=    0
																						_UpperCAmelCase       :		Optional[Any] 		=    1
																						_UpperCAmelCase       :		List[Any] 		=    0
																						_UpperCAmelCase       :		Dict 		=    1

																		# make sure weights are tied in PyTorch
																		pt_model.tie_weights()

																		with torch.no_grad():
																						_UpperCAmelCase       :		Tuple 		=    pt_model(**A_	).to_tuple()

																		_UpperCAmelCase       :		List[str] 		=    fx_model(**A_	).to_tuple()
																		self.assertEqual(len(A_	)						,					len(A_	)						,					"Output lengths differ between Flax and PyTorch"	)
																		for fx_output, pt_output in zip(A_						,					A_	):
																						self.assert_almost_equals(fx_output[:, -1]						,					pt_output[:, -1].numpy()						,					4e-2	)

																		with tempfile.TemporaryDirectory() as tmpdirname:
																						fx_model.save_pretrained(A_	)
																						_UpperCAmelCase       :		List[Any] 		=    pt_model_class.from_pretrained(A_						,					from_flax=A_	)

																		with torch.no_grad():
																						_UpperCAmelCase       :		Any 		=    pt_model_loaded(**A_	).to_tuple()

																		self.assertEqual(
																		    len(A_	)						,					len(A_	)						,					"Output lengths differ between Flax and PyTorch"	)
																		for fx_output, pt_output in zip(A_						,					A_	):
																						self.assert_almost_equals(fx_output[:, -1]						,					pt_output[:, -1].numpy()						,					4e-2	)





						@tooslow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										for model_class_name in self.all_model_classes:
														_UpperCAmelCase       :		Optional[int] 		=    model_class_name.from_pretrained("EleutherAI/gpt-j-6B"	)
														_UpperCAmelCase       :		Dict 		=    model(np.ones((1, 1)	)	)
														self.assertIsNotNone(A_	)





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

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


